1. Bibliographic Information

1.1. Title

The title of the paper is: STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents.

1.2. Authors

The authors of the paper are:

• Yue Chen

• Chen Huang

• Yang Deng

• Wenqiang Lei

• Dingnan Jin

• Jia Liu

• Tat-Seng Chua

  Their affiliations include:

• College of Computer Science, Sichuan University, China

• Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education, China

• National University of Singapore, Singapore

• Ant Group, China

1.3. Journal/Conference

The paper is published as a preprint on arXiv. The Published at (UTC): 2024-05-20T14:28:25.000Z indicates it was made public on this date. While arXiv is a preprint server and not a peer-reviewed journal or conference, papers published there are often submitted to reputable conferences or journals in the field of Natural Language Processing (NLP) and Information Retrieval (IR) due to the nature of the research (e.g., EMNLP, ACL, SIGIR, WWW). The involvement of authors from prestigious institutions like the National University of Singapore suggests the research is of academic rigor.

1.4. Publication Year

The paper was published in 2024.

1.5. Abstract

The abstract introduces the increasing importance of equipping conversational search engines with strategies for asking clarification questions across diverse domains. It notes that while Large Language Model (LLM)-based clarification strategies can rapidly transfer to various domains post-hoc, they often perform suboptimally on unseen domains, lacking effective domain transferability. The paper identifies that existing methods tend to produce one-size-fits-all strategies, limiting search effectiveness. To address this, it proposes a novel method called STYLE (rapid tranSfer To previouslY unseen domains via tailored stratEgies). STYLE aims to achieve effective domain transferability. Experimental results demonstrate that STYLE significantly improves search performance by approximately 10% on four unseen domains, showcasing its strong domain transferability.

1.6. Original Source Link

• Original Source Link: https://arxiv.org/abs/2405.12059
• PDF Link: https://arxiv.org/pdf/2405.12059v2.pdf
• Publication Status: This is a preprint on arXiv.

2. Executive Summary

2.1. Background & Motivation

2.1.1. Core Problem

The core problem the paper aims to solve is the domain transferability of Large Language Model (LLM)-based strategies for asking clarification questions in conversational search agents. Specifically, while LLMs possess strong context understanding and access to domain-specific knowledge, existing LLM-based methods struggle to maintain effective performance when applied to domains they have not been explicitly trained on (unseen domains).

2.1.2. Importance and Challenges

Equipping conversational search engines with effective strategies for when to ask clarification questions is crucial across various domains. User queries often contain ambiguities that can only be resolved through additional information. For example, a conversational search system not confident with financial jargon might perceive financial terminology as ambiguous. LLMs have shown promise in this area due to their ability to understand context and leverage domain-specific knowledge, allowing for rapid, post-hoc transfer (transfer after initial training without further domain-specific fine-tuning) to new domains.

However, the authors identify a significant challenge: empirical evidence suggests that these LLM-based methods often perform suboptimally in unseen domains. The underlying cause, which the paper investigates, is that current LLM-based methods tend to produce one-size-fits-all strategies. This means they apply the same general approach to clarification questions regardless of the specific nuances or requirements of a new domain, leading to limited search effectiveness. The mismatched distribution of domain-specific representations (how knowledge and concepts are represented within a particular domain) further impedes effective transfer.

2.1.3. Paper's Entry Point / Innovative Idea

The paper takes the first step to investigate the issue of domain transferability in LLM-based clarification strategies. Its innovative idea is to move beyond one-size-fits-all strategies by proposing a novel method, STYLE, that generates tailored strategies for previously unseen domains. This is achieved through two main components: a domain-invariant strategy planner (DISP) to extract general, structural information, and a multi-domain training (MDT) paradigm to enhance generalization.

2.2. Main Contributions / Findings

2.2.1. Primary Contributions

The paper makes three key contributions:

1. Identification of the Problem: It verifies and highlights that one-size-fits-all strategies significantly impede the domain transferability of existing LLM-based methods when deciding when to pose clarification questions.
2. Proposed Novel Method (STYLE): It introduces STYLE, a new method designed to improve domain transferability in a post-hoc manner. STYLE integrates a domain-invariant strategy planner (DISP) within the search engine and utilizes a multi-domain training (MDT) paradigm.
3. Experimental Validation: It experimentally demonstrates that STYLE exhibits strong domain transferability, leading to a significant average search performance improvement of approximately 10% on four previously unseen domains.

2.2.2. Key Conclusions / Findings

The key findings and conclusions reached by the paper are:

• Existing LLM-based clarification strategies, while capable of rapid transfer, often adopt a one-size-fits-all approach, failing to adapt effectively to the unique needs of different domains. This limits their search effectiveness in unseen domains.
• STYLE successfully overcomes this limitation by developing tailored strategies for diverse domains. Its DISP component extracts domain-invariant information (such as encoded conversation context, retrieved documents, and retrieval ranking scores) that is general and structural, making it robust across domains.
• The MDT paradigm, inspired by population-based training, further boosts DISP's generalization capacity by training it across multiple diverse domains, enabling it to adapt to novel scenarios.
• The effectiveness of STYLE is empirically validated, showing a notable performance improvement over leading LLM-based baselines in unseen domains. This improvement is attributed to STYLE's ability to customize its strategies, aligning them more closely with optimal in-domain strategies.
• STYLE's design, specifically its DISP and MDT components, addresses the challenge of mismatched distribution of domain-specific representations, paving the way for more efficient and adaptable conversational search agents.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

3.1.1. Large Language Models (LLMs)

Large Language Models (LLMs) are advanced artificial intelligence models, typically based on the Transformer architecture, that are trained on vast amounts of text data. They are capable of understanding, generating, and processing human language with remarkable fluency and coherence. Key capabilities relevant to this paper include context understanding (interpreting the meaning of text based on its surrounding words) and access to domain-specific sources of knowledge (their training data often contains diverse information, allowing them to recall facts or patterns relevant to particular fields).

3.1.2. Conversational Search Engines

A conversational search engine is an information retrieval system that allows users to interact with it using natural language, often in a multi-turn dialogue. Unlike traditional search engines that respond to single queries, conversational search engines can maintain context, clarify ambiguities, and refine results over several exchanges. The goal is to provide more relevant and satisfying answers by understanding user intent through dialogue.

3.1.3. Clarification Questions

Clarification questions are questions posed by a conversational agent to a user when the user's original query is ambiguous, underspecified, or could have multiple interpretations. The purpose is to gather additional information from the user to narrow down their intent and provide more accurate results. For example, if a user asks "Tell me about 'Sat'," a clarification question might be "Do you mean the planet Saturn, the Scholastic Assessment Test (SAT), or something else?"

3.1.4. Domain Transferability

Domain transferability refers to the ability of a machine learning model or system to perform well on a new domain (a specific area of knowledge or application, e.g., finance, e-commerce, movies) without requiring extensive retraining or fine-tuning on data from that new domain. High transferability means the model can generalize its learned strategies or knowledge effectively to unseen domains.

3.1.5. Markov Decision Process (MDP)

A Markov Decision Process (MDP) is a mathematical framework used to model decision-making in situations where outcomes are partly random and partly under the control of a decision-maker. It is often used in reinforcement learning. An MDP is defined by:

• A set of states $S$: All possible situations the agent can be in.

• A set of actions $A$: All possible moves the agent can make.

• A transition function $P(s', r | s, a)$: The probability of moving to state $s'$ and receiving reward $r$ after taking action $a$ in state $s$.

• A reward function R(s, a, s'): The immediate reward received for taking action $a$ in state $s$ and transitioning to state $s'$.

• A discount factor $\gamma$: A value (between 0 and 1) that determines the present value of future rewards.

  In the context of this paper, the conversational search process is framed as an MDP, where the system's states include the conversation history and retrieved documents, and its actions are either asking a clarification question or providing answers.

3.1.6. Reinforcement Learning (RL)

Reinforcement Learning (RL) is an area of machine learning concerned with how intelligent agents should take actions in an environment to maximize the cumulative reward. An RL agent learns by trial and error, receiving rewards for desired behaviors and penalties for undesired ones. The agent learns a policy (a strategy for choosing actions based on the current state) that optimizes its long-term rewards. Dueling Q-network is a specific architecture used in Deep Q-Networks (DQNs) for RL, which separates the estimation of state-value and advantage functions to improve learning stability and efficiency.

3.1.7. BERT (Bidirectional Encoder Representations from Transformers)

BERT is a powerful pre-trained language model developed by Google. It is based on the Transformer architecture and is designed to understand the context of words in a sentence by considering both the words that come before and after it (bidirectional). BERT can be fine-tuned for a wide range of NLP tasks, including encoding textual information into meaningful numerical representations (embeddings). In this paper, BERT is used to encode conversation history and retrieved documents.

3.1.8. Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is an architecture that combines a retrieval component with a generation component, often an LLM. When an LLM is asked a question, instead of generating an answer solely from its internal knowledge, a RAG system first retrieves relevant documents or information from an external knowledge base. This retrieved information is then fed to the LLM to help it generate a more accurate, up-to-date, and grounded response. The paper leverages a retrieval-augmented paradigm to obtain documents matching user queries.

3.2. Previous Works

The paper primarily focuses on LLM-based methods for asking clarification questions in conversational search, highlighting their limitations in domain transferability. The Related Work section and Introduction mention several key prior studies:

• Traditional Conversational Search & Clarification:

  • Aliannejadi et al. (2021, 2020, 2019): These works contribute to the foundation of conversational search and clarification, including building open-domain dialogue corpora with clarifying questions (ClariQ dataset mentioned in the paper) and investigating when to ask such questions. They laid the groundwork for understanding ambiguity in user queries.
  • Rahmani et al. (2023): This is a survey on datasets for clarification questions in conversational systems, indicating the growing interest and complexity of this area.
  • Wang and Ai (2021, 2022): These studies focus on controlling the risk of conversational search via reinforcement learning and simulating user behavior, providing methods for evaluating conversational systems.

• LLM-based Clarification Strategies: These are the primary baselines and the focus of the paper's critique regarding domain transferability.

  • Deng et al. (2022, 2023a, 2023b): This line of work extensively explores LLMs in conversational search.
    • (2022) PACIFIC: Focuses on proactive conversational question answering over tabular and textual data, demonstrating LLMs' context understanding capability.
    • (2023a) ProCoT: This method detects ambiguity and generates questions using few-shot Chain-of-Thought (CoT) prompting. The paper uses ProCoT as a leading LLM-based baseline. CoT involves prompting an LLM to generate a series of intermediate reasoning steps before arriving at a final answer, which can improve the quality of its output.
    • (2023b) Plug-and-play policy planner: This work explicitly mentions that LLM-based methods struggle with unseen domains, motivating the current paper.
  • Kuhn et al. (2022) CLAM: This method identifies when to ask clarification questions and generates them using few-shot in-context learning. In-context learning means giving the LLM a few examples of input-output pairs in the prompt, allowing it to learn the desired task without explicit fine-tuning. CLAM is a key baseline in this paper.
  • Zhang and Choi (2023) ClarSim: This method determines when to inquire using uncertainty modeling through self-questioning with LLMs. Self-questioning refers to an LLM's ability to internally query itself or generate questions to resolve its own uncertainties before providing an answer. ClarSim is also used as a baseline.

• Underlying LLM Technologies:

  • Devlin et al. (2018) BERT: The foundational BERT model is used for encoding information.
  • Reimers and Gurevych (2019) Sentence-BERT: Used for generating sentence embeddings, which are dense vector representations of sentences.
  • Nogueira and Cho (2019) monoBERT: A BERT-based cross-encoder for re-ranking documents.
  • Sun et al. (2023) ChatSearch: A ChatGPT-based retrieval method, representing state-of-the-art LLM-powered retrieval.

3.3. Technological Evolution

The evolution of conversational search systems, particularly regarding clarification questions, can be broadly traced as follows:

1. Early Heuristic/Rule-based Systems: Initial systems relied on hand-crafted rules or simple heuristics to detect ambiguity and generate questions. These were rigid and had poor generalization capabilities.

2. Supervised Machine Learning Methods: With the advent of machine learning, models were trained on annotated datasets to predict ambiguity or generate questions. These methods were more flexible but required extensive data annotation and training for each specific domain. Their domain transferability was limited as they would perform poorly on domains for which they hadn't seen data.

3. Reinforcement Learning for Conversational Strategies: Framed as Markov Decision Processes (MDPs), reinforcement learning allowed systems to learn optimal dialogue policies by interacting with a simulated or real environment, aiming to maximize conversation success. This provided a more dynamic approach to strategy learning.

4. Pre-trained Language Models (e.g., BERT): The emergence of powerful pre-trained models like BERT revolutionized NLP. These models could encode complex contextual information, improving retrieval quality and laying groundwork for advanced conversational understanding.

5. Large Language Models (LLMs) for Clarification: The latest stage involves leveraging the impressive context understanding and generation capabilities of LLMs (like GPT-3.5, ChatGPT). These models can perform tasks like ambiguity detection and question generation via in-context learning or Chain-of-Thought (CoT) prompting, offering rapid transfer capabilities to new domains in a post-hoc manner. This eliminates the need for extensive domain-specific fine-tuning.

   This paper's work fits into the fifth stage. It acknowledges the significant leap LLMs offer in post-hoc transfer but critically identifies their inherent limitation: the tendency to produce one-size-fits-all strategies that hinder true domain transferability to unseen domains.

3.4. Differentiation Analysis

Compared to the main methods in related work, especially the LLM-based baselines (ClarSim, CLAM, ProCoT), STYLE introduces several core differences and innovations:

• Addressing One-Size-Fits-All Strategies: The most significant differentiation is STYLE's explicit goal to overcome the one-size-fits-all limitation of existing LLM-based methods. While previous LLM-based methods might transfer some capability, they don't adapt their strategy to the specific needs of a new domain, leading to suboptimal performance. STYLE is designed to produce tailored strategies.

• Domain-Invariant Information Extraction: STYLE introduces a Domain-Invariant Strategy Planner (DISP). Unlike other methods that might directly process domain-specific semantic representations (which vary significantly across domains), DISP focuses on extracting domain-invariant information. This includes encoded conversation context and retrieved documents (processed by a fixed BERT encoder) along with retrieval ranking scores. The ranking scores are highlighted as relatively independent from the domain knowledge distributions, offering a generalizable signal about retrieval confidence and ambiguity. This approach mitigates the mismatched distribution challenge.

• Multi-Domain Training (MDT) for Generalization: STYLE employs a Multi-Domain Training (MDT) paradigm. Instead of training on a single domain or relying solely on the LLM's pre-trained knowledge, MDT explicitly trains the DISP across multiple, diverse domains. This is inspired by population-based training and is designed to enhance the generalization capability of the strategy planner, enabling it to better adapt to truly unseen domains. Existing LLM-based methods, while powerful, often don't have a structured training phase specifically geared towards learning to tailor strategies across a wide range of domains.

• Explicit Strategy Tailoring: The paper's analysis shows that STYLE explicitly tailors its strategies (e.g., probability of asking clarification questions) based on the asking benefits observed in different domains and at different conversation turns. This is a direct contrast to baselines like ProCoT (which might ask more questions as conversation advances, regardless of domain) or CLAM (which might maintain a consistent asking probability).

  In essence, STYLE innovates by creating a dedicated, trainable component (DISP) that learns to make clarification decisions based on robust, domain-invariant signals, and then generalizing this learning across many domains (MDT) to ensure adaptable strategies for any unseen domain.

4. Methodology

4.1. Principles

The core idea of STYLE is to achieve effective domain transferability for asking clarification questions in conversational search agents. It operates on two main principles:

1. Domain Invariance: To overcome the challenge of mismatched distribution of domain-specific representations, STYLE aims to identify and utilize domain-invariant information. This information is general and structural, meaning it captures aspects of the conversation state and retrieval confidence that are relevant across any domain, rather than being tied to specific domain knowledge. This makes the strategy planner robust to unseen domains.

2. Multi-Domain Generalization: Inspired by population-based training, STYLE postulates that training a strategy planner across a diverse set of domains will enhance its ability to generalize. This multi-domain training (MDT) paradigm encourages the planner to learn flexible, tailored strategies that can adapt to the unique requirements and asking benefits of novel environments, rather than defaulting to a one-size-fits-all approach.

   By combining these principles, STYLE seeks to develop a conversational agent that can rapidly transfer its clarification question strategy to previously unseen domains in a post-hoc manner (without requiring specific training data from the new domain), while still delivering tailored and effective performance.

4.2. Core Methodology In-depth (Layer by Layer)

The STYLE method comprises two main components: the domain-invariant strategy planner (DISP) and the multi-domain training (MDT) paradigm.

4.2.1. Problem Formulation

The conversational search process is framed as a Markov Decision Process (MDP), a common approach in reinforcement learning for sequential decision-making.

4.2.1.1. Retrieval-based Conversational Search

The system operates in a retrieval setting. For a user $u_i$, there's a target document $d_i$ in a collection $D$ that matches their intent.

• Interaction Start: The user provides an initial query $q_1$.
• Turn $t$: At each turn, given the user's current query $q_t$, the conversation history $H_t = \{q_1, m_1, ..., q_{t-1}, m_{t-1}, q_t\}$ is formed. Here, $q_{t-1}$ is the user's query and $m_{t-1}$ is the system's response from the previous turn.
• System Action: Based on $H_t$, the system first retrieves a subset of documents $D_t \subset D$. Then, using $H_t$ and $D_t$, the system generates a response $m_t$, which can be either:
  • Posing a clarification question $cq_t$ to the user.
  • Displaying the top $x$ retrieved documents from $D_t$.
• Iteration: This process continues until the system presents the correct document $d_i$ to the user or reaches a maximum number of turns $T$.

4.2.1.2. MDP Environment

The goal is to learn a strategy (or policy) $\pi$ that maximizes the expected total rewards over the conversation episodes. At turn $t$, considering the current query $q_t$, conversation history $H_t$, and retrieved documents $D_t$, the system chooses an action $a_t \in \mathcal{A}$ from a set of possible clarification strategies $\mathcal{A}$.

The optimal strategy $\pi^*$ is formulated as: $ \pi ^ { * } = \arg \operatorname* { m a x } _ { \pi \in \Pi } \mathbb E \left[ \sum _ { t = 0 } ^ { T } r ( s _ { t } , a _ { t } ) \right] $ Where:

• $\pi^*$: The optimal strategy (or policy) that maximizes expected total rewards.
• $\arg \operatorname*{max}_{\pi \in \Pi}$: The argument that maximizes the expression over all possible strategies $\pi$ in the set $\Pi$.
• $\mathbb{E}[\cdot]$: The expected value, considering the probabilistic nature of the environment and user responses.
• $\sum_{t=0}^{T}$: The sum over all turns from $t=0$ to the maximum turn $T$.
• $r(s_t, a_t)$: The immediate reward received for taking action $a_t$ in state $s_t$. This is denoted as $r_t$.
• $s_t$: Represents the current state of the conversation at turn $t$, which comprises the conversation history $H_t$ and the retrieved documents $D_t$.
• $a_t$: The action taken by the system at turn $t$ (either asking a clarification question or answering).

4.2.2. Overall Architecture

The overall architecture of STYLE is illustrated in Figure 1.

The following figure (Figure 1 from the original paper) shows the system architecture:

Figure 1: The STYLE contains domain-invariant strategy planner (DISP) and multi-domain training paradigm (MDT). The DISP extracts domain-invariant information and mitigates the swi of domain-specidistributions. The MDT encourages the domain transferability of DISP by population-based multi-domain training.

The process works as follows:

1. Multi-Domain Training (MDT): STYLE initially trains the Domain-Invariant Strategy Planner (DISP) across various domains using the MDT paradigm. This learning phase prepares DISP to generalize across different data distributions.
2. Post-hoc Transfer: Once DISP is well-trained, it can be applied to unseen domains without further domain-specific training.
3. Inference at Turn $t$ (Figure 1a):
   • LLM-based Retriever: Given the conversation context $H_t$, an LLM-based retriever identifies a set of documents $\mathbf{D}_t$ that are highly relevant to the user's query.
   • DISP Decision: Based on $H_t$ and $\mathbf{D}_t$, the DISP then decides whether to ask the user a clarification question by generating an action $a_t$. This decision is made using domain-invariant information.
   • LLM-based Generator:
     • If $a_t$ suggests asking, the conversational search engine uses an LLM-based generator to create a clarification question $cq_{t+1}$. This generation is often done via few-shot CoT (Chain-of-Thought), considering the conversation context and retrieved documents.
     • If $a_t$ suggests answering, the search engine presents the top $x$ retrieved documents to the user.

4.2.3. Domain-Invariant Strategy Planner (DISP)

To address the issue of mismatched distribution of domain-specific representations and enhance robustness across domains, the Domain-Invariant Strategy Planner (DISP) is introduced. It is implemented as a two-layer fully connected network.

The following figure (Figure 3 from the original paper) shows the structure of DISP:

Figure 3: Domain-invariant strategy planner (DISP).

Mechanism:

• Domain-Invariant Representation: DISP is configured to extract domain-invariant information that is general and structural. This information serves as the state $s_t$ for the MDP.

• Input Components: The domain-invariant information used in DISP is a concatenation of three main parts:

  1. Encoded Conversation Context $\mathbf{H}_t$: A fixed BERT model (which remains unchanged during training) encodes the conversation history $H_t$ into a representation $\mathbf{H}_t$.
  2. Encoded Retrieved Documents $\mathbf{D}_t$: The same fixed BERT model encodes the retrieved documents $D_t$ into a representation $\mathbf{D}_t$.
  3. Ranking Scores $score_t^{1:k}$: The ranking scores of the top $k$ retrieved documents (assigned by the retrieval module) are included. These scores are considered domain-invariant because they indicate the retrieval quality and confidence of the retrieval module, which is highly correlated with the ambiguous level of user queries. Importantly, these scores avoid introducing domain-specific semantic representation.

• Decision-Making: The concatenated domain-invariant information ($s_t$) is fed into the DISP (a two-layer MLP) to produce a value, which determines the action $a_t$.

  The value calculation is formulated as: $ value = MLP \left( \mathbf { H } _ { t } \oplus \mathbf { D } _ { t } \oplus s c o r e _ { t } ^ { 1 : k } \right) $ Where:

• value: The output of the Multi-Layer Perceptron (MLP), which represents the DISP's assessment based on the input.

• $MLP(\cdot)$: A Multi-Layer Perceptron, a type of feedforward artificial neural network used to process the input features.

• $\mathbf{H}_t$: The encoded representation of the conversation history at turn $t$. This is derived from the original conversation history $H_t$ (which includes $q_1, m_1, ..., q_{t-1}, m_{t-1}, q_t$) by a BERT encoder.

• $\mathbf{D}_t$: The encoded representation of the retrieved documents $D_t$ at turn $t$. This is derived from the subset of documents retrieved by the LLM-based retriever by a BERT encoder.

• $\oplus$: The concatenation operation, joining the vector representations $\mathbf{H}_t$, $\mathbf{D}_t$, and the ranking scores $score_t^{1:k}$ into a single feature vector.

• $score_t^{1:k}$: The ranking scores of the top $k$ retrieved documents at turn $t$. These scores indicate the relevance or confidence of the retrieval system.

  The action $a_t$ is determined by thresholding the value: $ a _ { t } = \left{ \begin{array} { l l } { ask , } & { value \ge 0 . 5 } \ { answer , } & { value < 0 . 5 } \end{array} \right. $ Where:

• $a_t$: The action chosen by the DISP at turn $t$.

• ask: The action to pose a clarification question to the user.

• answer: The action to provide the top $x$ retrieved documents as answers to the user.

• $value \ge 0.5$: If the value output by the MLP is greater than or equal to 0.5, the DISP decides to ask a clarification question.

• $value < 0.5$: If the value is less than 0.5, the DISP decides to answer by providing documents.

4.2.4. Multi-Domain Training (MDT)

To foster the domain transferability of DISP, STYLE incorporates the Multi-Domain Training (MDT) paradigm. This approach is inspired by population-based training, which suggests that training on diverse populations improves generalization.

Mechanism:

• Diverse Datasets: MDT trains the DISP using a diverse set of domain-specific datasets. Let $\mathbf{B} = \{B_1, B_2, ..., B_n\}$ represent $n$ distinct domain-specific datasets (e.g., e-commerce, web search, movies).

• Epoch-based Training: For each training epoch, a subset of these datasets is randomly selected as the training data. This exposes the DISP to an assortment of strategies relevant to different domains.

• Enhanced Generalization: This broad exposure during training bolsters DISP's capacity to tailor its strategy when faced with novel (unseen) scenarios.

• Inference on Unseen Domains: After training, the refined parameters of the DISP are retained, allowing it to make efficient inferences on any unseen domain $B^*$ (where $B^* \notin \mathbf{B}$).

• Interactive Reinforcement Learning: MDT employs interactive reinforcement learning. This involves an LLM-based user simulator (as described in prior research by Deng et al., 2023b) to simulate user interactions.

  • User Sample: Each sample consists of a user $u_i$ seeking a specific document $d_i$ with associated intent details $d_i^*$.

  • User Prompt Generation: The intent details $d_i^*$ and role instructions are used to formulate the user prompt $P_{user}$.

  • User Response: When the system presents a statement $m_{t+1}$ to user $u_i$ (e.g., a clarification question), the user simulator (an LLM like ChatGPT) responds with $q_{t+1}$.

    The user simulator's response $q_{t+1}$ is generated as follows: $ q _ { t + 1 } = L L M \left( P _ { u s e r } \left( d _ { i } ^ { * } \right) , m _ { t + 1 } , H _ { t } \right) $ Where:

• $q_{t+1}$: The user's response (query) at turn $t+1$.

• $LLM(\cdot)$: A Large Language Model (e.g., ChatGPT) acting as the user simulator.

• $P_{user}(d_i^*)$: The user prompt formulated using the user's intent details $d_i^*$ and role instructions.

• $m_{t+1}$: The system's statement or question presented to the user at turn $t+1$.

• $H_t$: The conversation history up to turn $t$.

• Reward Calculation: Upon receiving the user's response $q_{t+1}$, a reward $r_t$ is calculated based on predefined criteria.

• Dueling Q-Network Training: The Dueling Q-network is used for training the DISP. This is a variant of Q-learning, an off-policy reinforcement learning algorithm that learns the value of actions in states.

  The target value $y_t$ for updating the Q-network is expressed as: $ y _ { t } = \mathbb { E } _ { s _ { t + 1 } } \left[ r _ { t } + \gamma \operatorname* { m a x } _ { a \in \mathcal { A } } Q ^ { * } ( s _ { t + 1 } , a _ { t + 1 } ) | s _ { t } , a _ { t } \right] $ Where:

• $y_t$: The target Q-value for the state-action pair $(s_t, a_t)$. This is the value that the Q-network is trying to learn to predict.

• $\mathbb{E}_{s_{t+1}}[\cdot]$: The expectation over the next state $s_{t+1}$, meaning it averages over all possible next states according to their probabilities.

• $r_t$: The immediate reward received at turn $t$ for taking action $a_t$ in state $s_t$.

• $\gamma$: The discount factor (a value between 0 and 1, typically 0.99), which determines the present value of future rewards. A higher $\gamma$ makes the agent consider future rewards more heavily.

• $\operatorname*{max}_{a \in \mathcal{A}} Q^*(s_{t+1}, a_{t+1})$: The maximum Q-value for the next state $s_{t+1}$ across all possible actions $a_{t+1}$ in the action set $\mathcal{A}$. $Q^*$ here refers to the optimal Q-function, which DISP (the Q-network) is approximating. This term represents the estimated maximum future reward from the next state.

• $| s_t, a_t$: This notation indicates that the expectation is conditional on the current state $s_t$ and action $a_t$.

  This formula is the core of the Bellman equation for Q-learning, where $Q^*$ is the optimal Q-function that DISP aims to approximate. The Dueling Q-network architecture separates the estimation of state-value and advantage functions to improve the learning process of this Q-function.

5. Experimental Setup

5.1. Datasets

The experiments evaluate STYLE using four domain-specific benchmark datasets in conversational search. To simulate unseen domains, the evaluation employs a held-out approach: STYLE is trained on three datasets, and the remaining one is reserved as the unseen domain for testing. The datasets also contain unambiguous queries to test the strategy module's ability to correctly identify when clarification is not needed.

The following are the results from Table 1 of the original paper:

Details on each dataset and data processing (from Appendix C):

• ClariQ (Aliannejadi et al., 2021):

  • Domain: Web Track (general web search).
  • Characteristics: Contains conversations with an initial query, an ambiguity classification label (0: not ambiguous, 1: ambiguous), a clarification question, and a corresponding facet aligning with user intent.
  • Data Processing: The facet is treated as the ground truth document $d_i$. ChatGPT is used to rephrase $d_i$ into intent information $d_i^*$. To increase complexity and ensure a preponderance of ambiguous queries, a portion of conversations with non-ambiguous initial queries ($q_i^{ini}$) were removed.
  • Scale: 1000 conversations after processing.
  • Ambiguity: 0.60 (60% ambiguous queries).

• FaqAnt (Chen et al., 2023):

  • Domain: E-commerce (financial domain, specifically conversational FAQ).
  • Characteristics: Conversations with an initial query, an ambiguity label, and an FAQ question-answer pair matching user intent.
  • Data Processing: The question-answer pair is taken as the ground truth document $d_i$. ChatGPT paraphrases $d_i$ into $d_i^*$. Similar to ClariQ, conversations with less ambiguous queries were removed.
  • Scale: 3380 conversations after processing.
  • Ambiguity: 0.52 (52% ambiguous queries).

• MSDialog (Qu et al., 2018):

  • Domain: Microsoft Products (question-answering conversations from Microsoft forum).
  • Characteristics: Dialogues from a forum with multiple participants, including initial user queries and responses from Microsoft agents. Each turn has a binary label indicating if it's the right answer.
  • Data Processing: Ground truth document $d_i$ is determined by the response with the highest vote count. ChatGPT rephrases $d_i$ into $d_i^*$. Conversations where $d_i$ could be easily retrieved by BM25 from the first turn query were removed to ensure sufficient ambiguity.
  • Scale: 1948 conversations after processing.
  • Ambiguity: 0.53 (53% ambiguous queries).

• Opendialkg (Moon et al., 2019):

  • Domain: Books & Movie (recommendation/opinion seeking dialogues).

  • Characteristics: Dialogues where users seek recommendations or opinions on movies, music, or books. Originally used for conversational reasoning and knowledge graph entity prediction.

  • Data Processing: Human review establishes ground truth document $d_i$. ChatGPT rearticulates $d_i$ into $d_i^*$.

  • Scale: 1507 conversations after processing.

  • Ambiguity: 0.50 (50% ambiguous queries).

    These datasets were chosen because they represent diverse domains and contain a sufficient proportion of ambiguous queries, making them suitable for evaluating clarification strategies. The data processing steps further enhance their utility for testing the methods' ability to handle challenging, ambiguous conversational search scenarios.

5.2. Evaluation Metrics

The paper focuses on evaluating the efficiency and effectiveness of the search engines, as good clarification strategies are expected to lead to better search performance.

For every evaluation metric mentioned in the paper, the following provides a complete explanation:

5.2.1. Recall@5

• Conceptual Definition: Recall@5 measures the proportion of ground truth documents (the user's desired document) that are present within the top 5 retrieved documents by the search engine at the end of a successful conversation. It assesses the effectiveness of the search system in finding relevant documents. A higher Recall@5 indicates better performance.
• Mathematical Formula: $ \mathrm{Recall@K} = \frac{\text{Number of queries where the target document is in top K retrieved}}{\text{Total number of queries}} $ In this paper, K=5.
• Symbol Explanation:
  • $K$: A predefined integer, representing the number of top retrieved documents to consider (here, K=5).
  • Number of queries where the target document is in top K retrieved: The count of unique user queries for which the correct answer (ground truth document) was found among the highest-ranked K documents.
  • Total number of queries: The total number of user queries evaluated in the test set.

5.2.2. Average Turn (AvgT)

• Conceptual Definition: AvgT measures the average number of turns (interactions) required for the conversational search system to successfully find and present the user's desired document. It assesses the efficiency of the search process. A lower AvgT indicates a more efficient system.
• Mathematical Formula: $ \mathrm{AvgT} = \frac{\sum_{i=1}^{N} T_i}{N} $
• Symbol Explanation:
  • $N$: The total number of successful conversational search episodes.
  • $T_i$: The number of turns required to successfully find the target document for the $i$-th conversation.

5.2.3. Success Rate at Turn k (SR@k)

• Conceptual Definition: SR@k measures the proportion of conversations that successfully conclude (i.e., the target document is found) within or by a maximum of $k$ turns. It evaluates the overall effectiveness and efficiency combined. A higher SR@k indicates better performance. The paper uses SR@3 and SR@5.
• Mathematical Formula: $ \mathrm{SR@k} = \frac{\text{Number of conversations successfully completed by turn k}}{\text{Total number of conversations}} $
• Symbol Explanation:
  • $k$: A predefined integer, representing the maximum number of turns allowed for a successful conversation (here, k=3 and k=5).
  • Number of conversations successfully completed by turn k: The count of conversations where the correct answer was found within or by the $k$-th turn.
  • Total number of conversations: The total number of conversations evaluated in the test set.

5.2.4. Strategy Diversity

• Conceptual Definition: Strategy diversity is a metric introduced to quantify how varied the clarification strategies of a method are across different domains. The intuition is that an effective method for domain transferability should be able to produce distinct, tailored strategies for different domains, rather than a one-size-fits-all approach. A greater variety of strategies (higher diversity score) is desired.
• Mathematical Formula: The strategy diversity is quantified by calculating the average Dynamic Time Warping (DTW) distance between pairs of strategy trajectories (sequences of multi-turn actions). For a set of $N$ strategy trajectories $\{tr_1, tr_2, \dots, tr_N\}$, the average DTW distance is: $ \mathrm{Strategy,Diversity} = \frac{\sum_{i=1}^{N-1} \sum_{j=i+1}^{N} dtw(tr_i, tr_j)}{\frac{N(N-1)}{2}} $ The paper provides an example for 4 trajectories: $ \frac { d t w \left( t r _ { 1 } , t r _ { 2 } \right) + d t w \left( t r _ { 1 } , t r _ { 3 } \right) + . . . + d t w \left( t r _ { 3 } , t r _ { 4 } \right) } { 6 } $
• Symbol Explanation:
  • $tr_i$: The strategy trajectory for the $i$-th domain, represented as a sequence of actions (probabilities of asking clarification questions) across multiple conversation turns.
  • $dtw(tr_i, tr_j)$: The Dynamic Time Warping (DTW) distance between two strategy trajectories $tr_i$ and $tr_j$. DTW is an algorithm for measuring the similarity between two temporal sequences, which may vary in speed. A lower DTW distance indicates higher similarity, while a higher DTW distance indicates lower similarity (and thus higher diversity).
  • $N$: The total number of domains or strategy trajectories being compared.
  • $\frac{N(N-1)}{2}$: The total number of unique pairs of trajectories.

5.2.5. Asking Benefit

• Conceptual Definition: Asking benefit is a metric to quantify the usefulness of posing a clarification question at a specific turn. A good clarification question should help the search module retrieve the ground truth document more effectively. This benefit is measured by the improvement in the ranking of the ground truth document after the user answers the question.
• Mathematical Formula: $ gain _ { c q _ { t } } = r a n k _ { t } \left( d _ { i } \right) - r a n k _ { t + 1 } \left( d _ { i } \right) $
• Symbol Explanation:
  • $gain_{cq_t}$: The benefit (gain) achieved by asking clarification question $cq_t$ at turn $t$.
  • $rank_t(d_i)$: The rank of the user's desired document $d_i$ at turn $t$, before the clarification question $cq_t$ is asked and answered.
  • $rank_{t+1}(d_i)$: The rank of the user's desired document $d_i$ at turn $t+1$, after the user has answered clarification question $cq_t$.
  • A positive gain indicates that the clarification question improved the rank of the desired document (e.g., moved it from rank 10 to rank 5, so $10-5=5$). A negative gain means the rank worsened.

5.3. Baselines

The paper compares STYLE against two main classes of baselines to gain insights into the impact of clarification questions and domain transferability.

5.3.1. Retrieval-based Conversational Search Models (without Clarification Questions)

These models always provide answers to users and do not ask clarification questions. They represent the performance ceiling (or baseline) of retrieval without active ambiguity resolution.

• BM25: A statistics-based method for document retrieval, widely used as a strong baseline in information retrieval. It computes a score for each document based on the query terms' frequency and inverse document frequency.
• senBERT (Sentence-BERT, Reimers and Gurevych, 2019): Uses siamese and triplet BERT networks to encode input texts (queries and documents) into dense vector embeddings. It is particularly good at semantic similarity tasks. The implementation uses a publicly available checkpoint fine-tuned on an open-domain corpus like MS MARCO.
• monoBERT (Nogueira and Cho, 2019): A BERT-based cross-encoder re-ranker. Unlike senBERT which encodes query and document independently, a cross-encoder passes the concatenated query and document through BERT to generate a relevance score, capturing finer-grained interactions. This often leads to higher accuracy but is computationally more expensive.
• ChatSearch (Sun et al., 2023): A ChatGPT-based retrieval method that represents the state-of-the-art in LLM-powered retrieval. It uses permutation generation prompts to leverage the generative capabilities of LLMs for re-ranking documents.

5.3.2. LLM-based Methods (with Clarification Questions)

These methods decide whether to present retrieved documents or ask clarification questions to the user, leveraging LLMs for this decision-making and question generation.

• ClarSim (Zhang and Choi, 2023): Determines when to inquire by uncertainty modeling through self-questioning using LLMs. For the paper's implementation, it uses a Self-Ask strategy where LLMs decide "Yes" or "No" to posing a question.

• CLAM (Kuhn et al., 2022): Identifies when to ask and generates questions through few-shot in-context learning. This means it's given a few examples of ambiguous queries and their corresponding clarification questions within the prompt to guide its behavior.

• CLAMzeroShot (Kuhn et al., 2022): A variant of CLAM that uses the same prompts but employs zero-shot learning, meaning no examples are provided in the prompt. It relies solely on the LLM's inherent knowledge.

• ProCoT (Deng et al., 2023a): Detects ambiguity and generates questions using few-shot Chain-of-Thought (CoT) prompting. CoT involves prompting the LLM to explain its reasoning steps before providing an answer, which can improve the quality of generated questions. The paper adapts it by replacing "grounded documents" with retrieved documents.

  These baselines represent a comprehensive comparison against both traditional and cutting-edge LLM-based approaches, allowing the authors to rigorously evaluate STYLE's contribution to domain transferability.

5.3.3. Implementation Details (from Appendix F.1-F.3)

F.1. Parameters of our method

• Data Split: 6:1:1 for training, validation, and testing.
• Training Data Sampling: Randomly sampled from datasets in multiple domains during training.
• Maximum Turn ($T$): 10.
• Number of Training Episodes: 1800.
• DQN Parameters:
  • Experience buffer size: 10000.
  • Sample size: 32.
• Optimizer: Adam.
• Learning Rate: $1 \times 10^{-4}$.
• Discount Factor ($\gamma$): 0.99.
• Rewards:
  • Successful search: 1.0.
  • Exceeding maximum turns: -0.5.
• Number of Presented Documents ($x$): 5.
• BERT-based Encoder Layers (in DISP): 3 layers.

F.2. Baseline Implementation

• BERT-based Baselines (senBERT, monoBERT):
  • Initialized with publicly available checkpoints from Hugging Face, pre-trained on open-domain corpora (e.g., MS MARCO).
  • Fine-tuned on the same training source as STYLE (i.e., not in the same domain as the test set to simulate transferability scenarios).
  • Learning rate: $5 \times 10^{-5}$.
  • Epochs: 15.
  • Batch size: 16.
  • Optimizer: AdamW.
• LLM-based Methods (ClarSim, CLAM, CLAMzeroShot, ProCoT):
  • Implemented using gpt-3.5-turbo.
  • CLAM: Adheres to prompts from the original paper, using few-shot in-context learning for clarification need prediction and question generation.
  • ClarSim: Diverges from the original paper's exact method due to lack of decoder entropy/intended interpretation. Instead, applies a Self-Ask strategy where LLMs decide "Yes" or "No" to asking a question.
  • ProCoT: Adapts the original method by replacing "grounded documents" with the retrieved documents and uses few-shot Chain-of-Thought (CoT) prompts for inquiry.
  • CLAMzeroShot: Uses the same prompts as CLAM but with zero-shot in-context learning (no examples).
  • Prompts for LLM-based methods are detailed in Figures 8 & 9 of the Appendix (not provided in the prompt but mentioned).

F.3. Implementation of User Simulators

• Motivation: To address the challenge of evaluating multi-turn conversational systems, LLM-based user simulators are employed.
• Simulator Model: ChatGPT (gpt-3.5-turbo) is used as the user simulator.
• Prompt Formulation: Given a user $u_i$ with intent information $d_i^*$, a user prompt $P_{user}$ is formulated using $d_i^*$ and role instructions.
• User Response Generation:
  • When the system asks a clarification question, $P_{user}$ is sent to ChatGPT, which generates the user's answer.
  • When the system provides retrieved documents, the user simulator simply gives a positive or negative response based on whether the user's desired document $d_i^*$ is present in the provided documents.
• The prompt design for the user simulator is presented in Figure 10 of the Appendix (not provided in the prompt but mentioned).

6. Results & Analysis

6.1. Core Results Analysis

6.1.1. Evaluation on Unseen Domains (RQ1)

This section evaluates STYLE's domain transferability by assessing its conversational search performance on unseen domains.

The following are the results from Table 2 of the original paper:

Table: Evaluation on unseen domains.We mark best results in bold and underline the second-best ones. We pemult su n me than .0Tet m i presented in the Appendix G.

Analysis of Table 2:

• Superior Performance of STYLE: Across all four unseen domains (ClariQ, FaqAnt, MSDialog, Opendialkg), STYLE consistently achieves the best performance in terms of SR@5 and AvgT. For example, on ClariQ, STYLE reaches an SR@5 of 0.8655 with an AvgT of 3.8403, significantly outperforming the second-best ProCoT (SR@5 0.7563, AvgT 4.4986).

• Significant Improvement: On average, STYLE surpasses the leading LLM-based baseline (ProCoT) by approximately 10% in SR@5 across all domains. It also maintains a lead of over 5% in AvgT compared to baselines in most domains, indicating both higher accuracy and greater efficiency.

• Robust Transferability: STYLE demonstrates strong domain transferability even in domains where clarification questions might be less critical. For instance, on MSDialog, ChatSearch (a retrieval-based method without clarification) outperforms other LLM-based methods, suggesting that clarification might not be universally beneficial. Nevertheless, STYLE still manages to surpass ChatSearch (SR@5 0.6511 vs. 0.6378), underscoring its robust adaptability to diverse domain needs.

• Clarification is Beneficial (mostly): Generally, LLM-based methods (including STYLE) that leverage clarification questions tend to outperform retrieval-based methods without clarification (BM25, senBERT, monoBERT, ChatSearch) in SR@5 and AvgT, especially on domains like ClariQ and FaqAnt. This confirms the value of asking clarification questions when implemented effectively.

  In summary, the results strongly validate STYLE's ability to effectively transfer to unseen domains, achieving superior search accuracy and efficiency by adapting its clarification strategies.

6.1.2. In-domain Training Analysis (Appendix D)

This analysis from Appendix D investigates STYLE's performance when in-domain training data is available, comparing it with supervised baselines.

The following are the results from Table 6 of the original paper:

Table: In-mraianalysisThe ubsciptiDdicat that thismetho wastrainetheme unsm whehe valuai s perorWearkhevlu dicahe est peoann the second-best performance in underline.

Analysis of Table 6:

• Higher Upper Bound for STYLE: When sufficient in-domain training data is available (as represented by STYLEinDomain), STYLE achieves significantly higher performance compared to senBERTinDomain and monoBERTinDomain. This indicates that STYLE's underlying architecture and strategy learning mechanism have a higher performance upper bound when they can be precisely tailored to a specific domain. For example, on ClariQ, STYLEinDomain achieves an SR@5 of 0.8739, compared to 0.6975 for senBERTinDomain.

• Robust Transferability: STYLE (the version trained on multiple domains and applied to unseen ones) still performs very close to, and sometimes even surpasses, STYLEinDomain (e.g., on MSDialog, STYLE has a higher SR@5 of 0.6511 compared to STYLEinDomain's 0.6400, and a lower AvgT). More notably, STYLE consistently outperforms senBERTinDomain and monoBERTinDomain even when these baselines are trained in-domain. This demonstrates STYLE's robust transferability and its ability to maintain superior performance without relying on domain-specific training data for the target domain. This is a crucial finding, as annotating data for clarification question models is a costly process.

  This analysis reinforces that STYLE's approach allows it to achieve strong performance in unseen domains while having a high potential performance when in-domain data is available for training.

6.2. Strategy Characteristics Analysis (RQ2)

This section verifies whether STYLE produces tailored strategies for different domains, a key claim for its effectiveness. It compares STYLE with STYLEinDomain (a version trained specifically on the target domain, representing an ideal tailored strategy) and LLM-based baselines.

The following figure (Figure 4 from the original paper) illustrates strategy trajectories:

Figure 4: Strategy trajectory illustration on two best LLM-based methods. The $\mathbf { X }$ -axis indicates the conversation turns. The Y-axis indicates the probability of asking. The strategy diversities is as follows STYLE: 0.9187, Pr0CoT: 0.6079, CLAM: 0.4459.

The following are the results from Table 3 of the original paper:

Table 3: The DTW similarities to STYLEinDomain. Lower DTW corresponds to a better alignment with the strategy used in $\mathbf { STYLE } _ { i n D o m a i n }$.

Analysis of Figure 4 and Table 3:

• LLM-based Baselines (One-Size-Fits-All):
  • Figure 4 visually confirms that ProCoT maintains a consistent strategy across domains: a tendency to ask increasingly more questions as the conversation progresses.
  • CLAM also shows a uniform strategy, maintaining a consistent likelihood of asking questions at each turn, regardless of the domain.
  • Quantitatively, Table 3 shows that CLAM and ProCoT have significantly higher DTW (Dynamic Time Warping) similarities to STYLEinDomain compared to STYLE. A higher DTW score here indicates less alignment with the in-domain tailored strategy. For instance, CLAM's DTW on ClariQ is 3.8850, and ProCoT's is 2.5955, both much higher than STYLE's 0.5904. This means they adopt one-size-fits-all strategies that are not tailored to the specific domain.
• STYLE Produces Diverse and Tailored Strategies:

  • Figure 4 illustrates that STYLE exhibits the highest level of strategy diversity (0.9187) compared to ProCoT (0.6079) and CLAM (0.4459). This visual diversity is crucial.

  • More importantly, STYLE's clarification strategies closely align with those of STYLEinDomain (the ideal, domain-specific strategy). For example, on Opendialkg, both STYLE and STYLEinDomain tend to ask clarification questions early in the conversation and then gradually reduce the frequency. This trend is observed across other datasets as well.

  • Table 3 quantitatively supports this, showing STYLE consistently achieves the lowest DTW similarity scores across all domains (e.g., 0.5904 for ClariQ, 0.0518 for MSDialog). This indicates a strong alignment with the in-domain tailored strategies.

    In conclusion, STYLE successfully customizes its clarification strategies to meet the diverse requirements of different domains, contrasting sharply with the one-size-fits-all approach of existing LLM-based baselines. This ability to tailor strategies is identified as the foundation for its superior domain transferability.

6.3. Characteristics of STYLE (RQ3)

This section delves into the reasons behind STYLE's effective domain transferability by analyzing the asking benefits (the positive impact of asking clarification questions) at each conversation turn.

The following figure (Figure 5 from the original paper) illustrates the average gain and probability of asking clarification questions:

Figure 5: Illustration on the average gain and the probability of asking clarification questions. The $\mathrm { X }$ -axis indicates the cvesain turs.The-axis (t)indicate theavere ski gai at each tur while the-axis ht) indicates the probability oski

Analysis of Figure 5:

• Fluctuating Asking Benefits: The figure shows that the average gain (benefit) from asking clarification questions varies significantly across domains and conversation turns for all methods. An effective system must adapt its strategy to these fluctuations.
• CLAM's Inflexibility: CLAM demonstrates a consistent probability of asking, largely independent of the conversation turn or the asking benefits. For instance, on MSDialog and FaqAnt, where the asking benefits noticeably decrease after the second turn, CLAM still maintains a relatively uniform asking probability. This highlights its inability to adjust its strategy to the actual utility of clarification questions in different contexts.
• STYLE's Precise Control and Adaptability: In contrast, STYLE exhibits precise control over its strategy, adjusting its probability of asking in direct response to the fluctuations in asking benefits on a turn-by-turn basis.

  • On ClariQ, as the asking benefits gradually decrease, STYLE reduces its probability of asking accordingly.

  • On MSDialog, where the asking benefits remain notably low (around -20, meaning asking questions generally harms performance), STYLE strategically limits its asking probability to a minimum level across all turns. This demonstrates STYLE's capability to discern when clarification is not beneficial and to avoid unnecessary questions.

    This analysis confirms that STYLE's domain transferability stems from its ability to learn and employ diverse strategies that are specifically tailored to the varying needs and asking benefits of different domains. By adapting its clarification strategy dynamically, STYLE maximizes the positive impact of clarification questions, leading to enhanced performance in unseen domains.

6.4. Ablation Study

An ablation study was conducted to ascertain the contribution of each module within STYLE. This involves removing or modifying specific components of the model and observing the impact on performance.

The following are the results from Table 4 of the original paper:

Table: Ablatin valuai. D she ey predicoor omaitranserabily LDomvariabi training dataset and domain-invariant input also matters. The contribution of CoT is minimal.

Analysis of Table 4:

• DISP (Domain-Invariant Strategy Planner) Significance (row a):
  • Removing the DISP (- w/o DISP planner) leads to the most substantial performance decrease across all metrics and domains. For instance, on ClariQ, SR@5 drops from 0.8655 to 0.7563, and AvgT increases from 3.8403 to 4.4986. This highlights that DISP is the single most critical component of STYLE, confirming its role in ensuring effective domain transferability by extracting domain-invariant information.
• Training Sources of MDT (Multi-Domain Training) (rows b & c):
  • Reducing the diversity of training datasets (e.g., w/ 1 domain or w/ 2 domains) significantly impacts STYLE's performance.
  • w/ 1 domain shows a decrease from STYLE's SR@5 (e.g., 0.8655 to 0.8291 on ClariQ).
  • w/ 2 domains also shows a drop, although generally less severe than w/ 1 domain (e.g., 0.8655 to 0.8488 on ClariQ).
  • This strongly reinforces the necessity of training STYLE on a sufficiently diverse set of domains through MDT to ensure robust transferability. The more varied the training data, the better the model's ability to generalize to unseen domains.
• Domain-Invariant Input of DISP (rows d & e):
  • Excluding Retrieved Documents (- w/o documents, row d): Removing the encoded retrieved documents ($\mathbf{D}_t$) from DISP's input generally diminishes performance across most domains (e.g., SR@5 on ClariQ drops from 0.8655 to 0.8151). This confirms that access to information from the retrieval module is important for DISP's decision-making. Notably, on FaqAnt, AvgT slightly improves, but SR@5 is still slightly lower, suggesting some nuance.
  • Excluding Document Scores (- w/o doc scores, row e): The absence of document scores ($score_t^{1:k}$) particularly undermines performance in all tested domains. For instance, SR@5 on ClariQ drops to 0.7647, and AvgT increases to 4.3908. This is a significant drop, almost as large as removing the entire DISP. This finding highlights the crucial role of retrieval scores as domain-invariant information. These scores are indicative of the retrieval model's confidence and document relevance, providing robust signals for DISP to make informed decisions about ambiguity, regardless of the specific domain content.
• Prompt Design (- w/o CoT, row f):

  • Changing the prompt design for question generation from Chain-of-Thought (CoT) to a simpler in-context learning approach (- w/o CoT) results in a slight decrease in performance (e.g., SR@5 on ClariQ from 0.8655 to 0.8319).

  • While CoT is beneficial, STYLE still retains superior performance even without it, affirming the overall robustness of the STYLE framework itself, beyond just the LLM prompting technique. This suggests that the core DISP and MDT components are the primary drivers of STYLE's success.

    In conclusion, the ablation study clearly demonstrates that the DISP and the multi-domain training approach, particularly the inclusion of domain-invariant retrieval scores, are fundamental to STYLE's effectiveness and its strong domain transferability.

6.1.3. Human Evaluation of Clarification Question (Appendix E)

This section from Appendix E details a human evaluation conducted to rigorously assess the quality of clarification questions generated by STYLE compared to top-performing LLM-based baselines (ProCoT and CLAM).

The following figure (Figure 7 from the original paper) presents the human evaluation results:

Figure 7: The human evaluation results of the quality of clarification questions. The y-axis represents the number of samples preferred by human judges.

Evaluation Setup:

• Methods Compared: STYLE, ProCoT, and CLAM.
• Sample Size: 100 randomly selected clarification questions from each method.
• Context: Samples included conversation context, retrieved documents, and user intent information.
• Raters: Three independent human raters.
• Criteria:
  1. Helpfulness: Whether the question is informative and likely to elicit valuable information from the user.
  2. Intent Consistency: How well the question aligns with the user's underlying intent (e.g., includes relevant keywords).
• Inter-rater Reliability: Measured using Fleiss' Kappa.
  • Helpfulness: 0.517 (moderate agreement).
  • Intent Consistency: 0.782 (substantial agreement).

Analysis of Figure 7:

• The bar charts visually represent the number of samples preferred by human judges for each criterion.

• Helpfulness: STYLE's generated questions are preferred over ProCoT and CLAM in terms of Helpfulness. The bars for STYLE are noticeably higher, indicating that humans found its questions more informative and valuable.

• Intent Consistency: Similarly, STYLE's questions are preferred for Intent Consistency. This means its questions were better at incorporating elements pertinent to the user's intended purpose.

  Conclusion: The human evaluation results indicate that STYLE not only achieves superior search performance but also generates clarification questions of higher quality (more helpful and consistent with user intent) compared to other leading LLM-based methods. This contributes to the overall effectiveness of STYLE.

6.1.4. Runtime Analysis (Appendix G)

This section from Appendix G analyzes the runtime performance, specifically the average time required per turn, for STYLE and other methods.

The following are the results from Table 7 of the original paper:

Table 7: The runtime analysis. STYLE takes only 1.5773 seconds on average per turn, which is less than other methods.

Analysis of Table 7:

• STYLE demonstrates the lowest Runtime Per Turn at 1.5773 seconds, making it the most efficient method among those evaluated.

• This efficiency is attributed to the use of a lightweight model for its strategy module (DISP), which is a two-layer fully connected network, instead of relying on the computationally intensive LLMs for every decision, as is common in prior approaches.

• The other LLM-based methods (ClarSim, CLAMzeroShot, CLAM, ProCoT) all have higher per-turn runtimes, ranging from 1.8269s (CLAM) to 3.3355s (ClarSim). This suggests that repeated LLM calls for decision-making and generation can incur significant latency.

  The runtime analysis shows that STYLE offers a practical advantage by reducing latency, which is crucial for real-time conversational agents, without sacrificing performance.

7. Conclusion & Reflections

7.1. Conclusion Summary

This paper rigorously investigates and addresses a critical limitation in current Large Language Model (LLM) powered conversational agents: their struggle with domain transferability when deciding when to ask clarification questions in unseen domains. The authors first confirm that existing LLM-based methods often employ one-size-fits-all strategies, which limits their effectiveness outside of their training domains.

In response, the paper introduces STYLE (rapid tranSfer To previouslY unseen domains via tailored stratEgies), a novel method designed to achieve robust domain transferability. STYLE comprises two key innovations: a Domain-Invariant Strategy Planner (DISP) and a Multi-Domain Training (MDT) paradigm. DISP extracts general and structural domain-invariant information (like encoded conversation context, retrieved documents, and crucial retrieval ranking scores) to mitigate domain-specific representation mismatches. MDT, inspired by population-based training, trains DISP across multiple diverse domains to enhance its generalization capabilities.

Extensive experiments on four distinct conversational search benchmark datasets (ClariQ, FaqAnt, MSDialog, Opendialkg) validate STYLE's effectiveness. It consistently outperforms leading LLM-based baselines, achieving an average search performance improvement of approximately 10% in SR@5 on unseen domains, while also being more efficient (lower AvgT and Runtime Per Turn). Further analysis reveals that STYLE's success stems from its ability to produce diverse and tailored strategies that adapt to the varying asking benefits of different domains and conversation turns, unlike the static approaches of its counterparts. A human evaluation also confirms that STYLE generates more helpful and intent-consistent clarification questions.

In essence, STYLE lays a strong foundation for future research in developing conversational agents that are not only powerful but also highly adaptable and robust across a wide array of application domains.

7.2. Limitations & Future Work

The authors acknowledge specific limitations and suggest future research directions:

7.2.1. Limitations

• Scope of Conversational Search: The current study focuses exclusively on conversational retrieval scenarios. The authors note that conversational search is multifaceted, encompassing question answering (QA), retrieval, and recommendation scenarios. A thorough analysis across all these settings would provide a more comprehensive understanding, but it would significantly increase the experimental workload and diverge from the paper's core research question.
• Held-out Evaluation Strategy: To simulate unseen domains and manage experimental workload, the held-out evaluation was conducted by training STYLE on three datasets and reserving one as the unseen domain test set for each model. This means that for each out-of-domain trained model, only one dataset at a time served as the unseen test set.

7.2.2. Future Work

• Expanding to Other Conversational Search Forms: The authors plan to extend their research to encompass other forms of conversational search, such as QA and recommendation, beyond just retrieval. This would involve adapting STYLE's framework to different interaction patterns and success metrics.
• Multiple Unseen Test Sets Simultaneously: To provide an even more rigorous validation of domain transferability, future work will consider using multiple datasets simultaneously as the test set for each out-of-domain trained model, rather than just one at a time. This would better reflect real-world scenarios where an agent might encounter several new domains.

7.3. Personal Insights & Critique

7.3.1. Inspirations

• Addressing a Critical Real-World Problem: The paper tackles a highly practical and significant problem: how to make LLM-powered conversational agents truly adaptable across diverse domains without prohibitive retraining costs. The one-size-fits-all issue is intuitive once pointed out, yet addressing it systematically with domain-invariant features and multi-domain training is elegant.
• The Power of Domain-Invariant Signals: The emphasis on retrieval ranking scores as a crucial domain-invariant signal is particularly insightful. It highlights that often, the meta-information about a model's confidence or an interaction's state can be more universally applicable than raw, domain-specific content. This principle could potentially be applied to other areas of LLM adaptation where semantic content varies wildly.
• Principled Approach to Generalization: The use of Multi-Domain Training (MDT) inspired by population-based training for improving generalization is a strong contribution. It moves beyond simply pre-training on large corpora to explicitly training for adaptability across diverse task distributions, which is a key challenge for real-world AI systems.
• Lightweight Decision-Making: The decision to use a lightweight DISP (a two-layer MLP) for the core strategy planner, rather than relying on another large LLM, is a smart design choice. It not only improves runtime efficiency but also grounds the decision process in specific, interpretable signals (like retrieval scores), making the system more controllable and robust.

7.3.2. Potential Issues / Unverified Assumptions / Areas for Improvement

• Reliance on LLM-based User Simulator: While LLM-based user simulators are becoming standard for evaluating conversational systems, they are not perfect substitutes for real human users. Their responses, though sophisticated, might still reflect biases or limitations of the LLM itself, potentially leading to an over-optimization for the simulator rather than for real users. The human evaluation helps mitigate this but is limited in scale.

• Definition of "Domain-Invariant": While retrieval scores are presented as domain-invariant, their effectiveness might still depend on the underlying retrieval model's quality across different domains. If the retriever itself struggles in an unseen domain, the domain-invariant scores might lose their predictive power. The paper assumes a capable LLM-based retriever, which itself needs transferability.

• Scalability of MDT: The Multi-Domain Training approach currently involves training on a few benchmark datasets. As the number and diversity of potential domains grow, the complexity and computational cost of MDT might increase significantly. Strategies for efficient multi-domain learning or continuous domain adaptation could be explored.

• Clarification Question Generation Quality: While STYLE generates better questions according to human evaluation, the core DISP decides when to ask. The actual generation of the clarification question is still offloaded to a generic LLM (via few-shot CoT). Future work could investigate how the DISP's learned strategy could more directly inform and improve the generative aspect of clarification.

• Generalizability of "Ambiguity": The paper defines ambiguity in terms of a single ground truth document. However, real-world ambiguity can be more complex, involving multiple relevant interpretations or nuanced user intent. How STYLE's domain-invariant approach handles these more complex forms of ambiguity could be an interesting area of study.

• Interpretation of "Asking Benefits": The asking benefits are calculated based on the change in rank of the desired document. This is a practical metric, but it implicitly assumes that improving the rank of the one correct document is the sole goal. In some conversational contexts, other benefits like user satisfaction, confidence building, or exploration might also be important, and these are not directly captured by the gain metric.

  Overall, STYLE makes a significant step forward in making LLM-powered conversational agents more robust and adaptable. Its principled approach to domain transferability by separating domain-invariant decision-making from domain-specific content holds great promise for the broader field of AI system design.