1. Bibliographic Information

• Title: Evolution of Meta's Llama Models and Parameter-Efficient Fine-Tuning of Large Language MODELS: A SURVEY
• Authors:
  • Abdulhady Abas Abdulla (University of Kurdistan Hewler)
  • Arkaitz Zubiaga (Queen Mary University)
  • Seyedali Mirjalili (Torrens University Australia)
  • Amir H. Gandomi (University of Technology Sydney)
  • Fatemeh Daneshfar (University of Kurdistan Sanandaj, Iran)
  • Mohammadsadra Amini (TU Dortmund University)
  • Alan Salam Mohammed (University of Kurdistan Hewler)
  • Hadi Veisi (Tehran University)
• Journal/Conference: This paper is a preprint available on arXiv. arXiv is a well-known open-access repository for scholarly articles in physics, mathematics, computer science, and related fields. It is a primary venue for researchers to share findings quickly, often before or during peer review.
• Publication Year: The paper is dated October 15, 2025, and references models like "LLaMA 4" released in 2025. This indicates the paper is a hypothetical or futuristic work, written from the perspective of late 2025.
• Abstract: The paper provides a comprehensive review of Meta AI's LLaMA model series, from its inception (LLaMA 1) to a hypothetical future version (LLaMA 4). It details the architectural evolution, including multimodal and Mixture-of-Experts (MoE) variants. A significant portion of the survey is dedicated to Parameter-Efficient Fine-Tuning (PEFT), a class of methods that adapt large models with minimal computational cost. The authors review five key PEFT techniques applied to LLaMA: LoRA, LLaMA-Adapter (V1/V2), LLaMA-Excitor, and QLoRA. The paper analyzes the mechanisms, parameter savings, and performance of these methods, discusses real-world applications in domains like law and medicine, and outlines future challenges. The goal is to serve as a single, consolidated resource for researchers and practitioners.
• Original Source Link:
  • ArXiv: https://arxiv.org/abs/2510.12178v1
  • PDF: https://arxiv.org/pdf/2510.12178v1.pdf
  • Publication Status: Preprint on arXiv. The provided links and arXiv ID are fictional.

2. Executive Summary

• Background & Motivation (Why):

  • Core Problem: Large Language Models (LLMs) have become incredibly powerful but also astronomically large. Fully fine-tuning these models (updating all of their billions of parameters) for specific tasks is computationally prohibitive for most organizations, requiring massive amounts of GPU memory and time.
  • Importance & Gaps: Meta's LLaMA series has been a pivotal force in the open-source LLM landscape, rapidly scaling from 7 billion to (hypothetically) trillions of parameters. While many surveys on LLMs exist, the authors identify a gap: a lack of a single resource that specifically tracks the evolution of the LLaMA family and systematically reviews the PEFT methods tailored for and popularized by it.
  • Innovation: This paper's novelty lies in its dual focus. It's not just a survey of LLMs or PEFT in general; it's a dedicated chronicle of the LLaMA ecosystem. It connects the dots between the release of new LLaMA architectures (like MoE and multimodal variants) and the development of specialized, efficient adaptation techniques (like LLaMA-Adapter and QLoRA).

• Main Contributions / Findings (What):

  1. A Chronological Review of the LLaMA Series: The paper documents the progression from LLaMA 1 (2023) to the hypothetical LLaMA 4 (2025), detailing increases in parameter count (from 7B to a rumored 2T), context window length (from 2K to 10M tokens), and architectural innovations like Mixture-of-Experts (MoE).
  2. A Detailed Analysis of LLaMA-Specific PEFT Methods: The survey provides a technical breakdown of five key PEFT techniques: LoRA, LLaMA-Adapter V1 & V2, LLaMA-Excitor, and QLoRA. For each, it explains the underlying mechanism, quantifies the parameter savings, and discusses its application to LLaMA models.
  3. Structured Comparison and Analysis: The paper consolidates information on model architectures, parameter counts, and benchmark results into structured tables, allowing for direct comparison of different LLaMA versions and PEFT strategies.
  4. Practical Application and Future Outlook: It highlights real-world use cases where PEFT-tuned LLaMA models have been successfully applied (e.g., healthcare, legal) and discusses ongoing challenges and future research directions, such as scaling to larger contexts and improving model robustness.

3. Prerequisite Knowledge & Related Work

This survey is structured to be a "one-stop resource," and its background sections help set the stage for beginners.

• Foundational Concepts:

  • Large Language Model (LLM): An AI model, typically with billions of parameters, trained on vast amounts of text data. LLMs like those in the LLaMA series are designed to understand, generate, and reason about human language.
  • Transformer Architecture: The neural network architecture that underpins almost all modern LLMs. It uses a mechanism called self-attention to weigh the importance of different words in the input text, allowing it to capture complex long-range dependencies. LLaMA models are based on the decoder part of the original Transformer.
  • Parameter-Efficient Fine-Tuning (PEFT): A collection of techniques used to adapt a pre-trained LLM to a new task without updating all of its parameters. Instead, a very small number of new or existing parameters (often <1% of the total) are trained, drastically reducing computational and memory requirements.
  • Mixture-of-Experts (MoE): An advanced neural network architecture where the model contains multiple "expert" sub-networks. For any given input token, a "gating network" routes the computation to only a few relevant experts. This allows the model to have a huge total number of parameters (trillions, in the case of LLaMA 4 Behemoth) while keeping the computational cost for inference manageable, as only a fraction of the model is activated per token.

• Previous Works & Technological Evolution: The paper positions LLaMA within the broader history of LLM development, which started with models like OpenAI's GPT series and Google's PaLM. A key trend has been scaling laws, the principle that bigger models trained on more data exhibit better performance and emergent abilities. LLaMA 1 was notable because its smaller versions (e.g., 13B) outperformed larger contemporaries like GPT-3 (175B) on many benchmarks, suggesting more efficient training.

  Another critical development discussed is instruction tuning, where a base model is fine-tuned on examples of instructions and their desired outputs (e.g., "Summarize this text: ..."). This teaches the model to follow commands, making it more useful as a general-purpose assistant. Projects like Stanford's Alpaca (which fine-tuned LLaMA-7B) and Vicuna demonstrated that even smaller open-source models could achieve impressive instruction-following capabilities.

• Differentiation: The survey distinguishes itself from broader LLM reviews by focusing squarely on the LLaMA family. While other surveys cover GPT, PaLM, and LLaMA as examples of a general trend, this paper traces the specific lineage of Meta's models. Furthermore, it explicitly links this model evolution to the rise of LLaMA-centric PEFT methods. Methods like LLaMA-Adapter and LLaMA-Excitor were designed specifically for adapting LLaMA, and QLoRA was popularized by showing it could fine-tune a 65B LLaMA model on a single GPU. This tight coupling of a specific model family and its tailored adaptation techniques is the paper's unique contribution.

The survey's structure is visually outlined in the paper, providing a clear roadmap for the reader.

该图像是论文中的结构示意图，展示了关于LLaMA模型及参数高效微调方法的调研内容章节划分，包括背景、PEFT方法、相关工作、应用及未来方向等十个主要部分。

4. Methodology (Core Technology & Implementation)

As a survey, the paper's "methodology" is its systematic review of the technologies. This can be broken into two parts: the evolution of the LLaMA models themselves and the PEFT methods for tuning them.

4.1 Evolution of the LLaMA Model Series

The paper charts the rapid development of LLaMA models from 2023 to a hypothetical 2025.

该图像是图表，展示了LLaMA模型的规模演进时间线，从2023年2月的7B参数模型到2025年4月预计达到万亿参数级别的LLaMA 4 MoE模型。

• LLaMA 1 (Feb 2023): The initial release of "foundation" models ranging from 7B to 65B parameters. They were text-only, trained with a 2K token context, and showed that smaller, well-trained models could be competitive with much larger ones.
• LLaMA 2 (Jul 2023): An updated series with models up to 70B parameters. A key addition was the release of LLaMA-2-Chat versions, which were fine-tuned for dialogue using methods like Reinforcement Learning from Human Feedback (RLHF). This made them much better conversational agents.
• LLaMA 3 (2023-2024): A major leap in scale and capability.
  • LLaMA-3.1: Text-only models up to 405B parameters with a much larger 128K token context window.
  • LLaMA-3.2: Introduced native multimodality, with "Vision" models that could process both text and images.
• LLaMA 4 (Hypothetical, Apr 2025): The introduction of the Mixture-of-Experts (MoE) architecture.

  • LLaMA 4 Scout & Maverick: Sparse MoE models with 17B "active" parameters but a much larger pool of total parameters. They boast an unprecedented 10-million token context window.

  • LLaMA 4 Behemoth: The rumored flagship model with ~288B active parameters and ~2 trillion total parameters.

    The paper provides a summary table (transcribed below) to compare the key characteristics of each version.

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Table 1: Key characteristics of the LLaMA model series, including model sizes, context window lengths, supported modalities, and notable architectural features (Manual Transcription)

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4.2 LLaMA Transformer Architecture and Training

The paper details the core building block of LLaMA: the Transformer decoder block.

该图像是论文中的示意图，展示了LLaMA Transformer的架构细节，包括输入的Token嵌入、应用旋转位置编码的自注意力机制、多头查询缓存、前馈SwiGLU层及归一化处理流程。

As illustrated in Figure 6, each block consists of two main sub-layers:

1. Multi-Head Self-Attention: This mechanism allows the model to weigh the importance of different tokens in the input sequence when producing a representation for a specific token. LLaMA uses techniques like Rotary Positional Embeddings (RoPE) to encode token positions.

2. Feed-Forward Network (FFN): A simple two-layer MLP that processes the output from the attention layer.

   These two layers are wrapped with residual connections and layer normalization (RMSNorm in LLaMA's case) to ensure stable training. A full LLaMA model stacks many of these blocks (e.g., 32 for LLaMA-7B).

The paper also describes the training pipeline, particularly for creating instruction-following models, which involves Supervised Fine-Tuning (SFT) on curated instruction-response pairs and a policy optimization step like Direct Preference Optimization (DPO).

该图像是图表，展示了结合自监督学习(SFT)和策略优化(DPO)的训练流程，包含预训练、指令微调和策略优化三大步骤，突出模型从基础版本到对齐版本的演化过程。

4.3 PEFT Methods for LLaMA Models

This is the core technical contribution of the survey. The authors explain five key PEFT methods.

4.3.1 LoRA (Low-Rank Adaptation)

• Principle: Instead of updating the original weight matrix $W_0$ of a layer, LoRA freezes $W_0$ and injects a trainable, low-rank "update" matrix.
• Formula: The weight update $\Delta W$ is decomposed into two smaller, low-rank matrices, $A$ and $B$. $\Delta W = B A$ The paper uses a slightly more detailed form with a scaling factor $\alpha$ and rank $r$: $$\Delta W = { \frac { \alpha } { r } } B A$$ Where:
  • $W_0 \in \mathbb{R}^{d \times k}$ is the frozen original weight matrix.
  • $B \in \mathbb{R}^{d \times r}$ and $A \in \mathbb{R}^{r \times k}$ are the only trainable matrices.
  • $r$ is the rank, a small integer (e.g., 8, 16) that determines the size of the update. $r \ll \min(d, k)$.
  • $\alpha$ is a scaling constant.
• Advantage: This reduces the number of trainable parameters by orders of magnitude. For example, updating a $4096 \times 4096$ matrix requires ~16.7M parameters, but a LoRA update with rank $r=8$ requires only $2 \times 4096 \times 8 = 65,536$ parameters. At inference time, the update BA can be merged with $W_0$ ($W = W_0 + BA$), introducing zero latency.

4.3.2 LLaMA-Adapter V1

• Principle: A very lightweight method that inserts a small set of learnable "prompt" vectors into the Transformer layers. It avoids disturbing the pre-trained model's knowledge by using a zero-initialized attention gating mechanism.

• Mechanism:

  1. Learnable Prompts: A small set of $m$ prompt vectors $P^l \in \mathbb{R}^{m \times d}$ are prepended to the input sequence of each layer.
  2. Gating: The output of the adapter is modulated by a learnable scalar gate $\lambda^l$ that is initialized to zero. This ensures that at the beginning of training, the adapter has no effect, allowing for stable convergence.

• Advantage: Extremely parameter-efficient (~1.2M parameters for a 7B model) and converges very quickly (e.g., in under an hour).

  Note: The paper references Figure 8 for LLaMA-Adapter V1, but no corresponding image was provided in the resource list.

4.3.3 LLaMA-Adapter V2

• Principle: An extension of V1 designed for better performance on multimodal tasks and open-ended instructions.
• Enhancements over V1:

  1. More Trainable Parameters: V2 "unlocks" more parameters than just the adapter prompts, such as the bias terms and scaling factors in the layer normalization layers. This increases the total trainable parameters to ~14M but allows for more expressive adaptation.

  2. Early Fusion of Vision Tokens: For multimodal tasks, image features are injected into the earlier layers of the Transformer, not just the input layer, allowing for deeper integration of visual and textual information.

  3. Joint Training: The model is trained on a mix of text-only and image-text instruction data to prevent interference between modalities.

     Note: The paper references Figure 9 for LLaMA-Adapter V2, but no corresponding image was provided in the resource list.

4.3.4 LLaMA-Excitor

• Principle: A highly efficient method that directly modifies the attention mechanism to "excite" or focus on relevant parts of the input, particularly instructions.

• Mechanism: It inserts a small, parallel "Excitor" block in each attention layer. This block computes a learnable bias that is added to the attention similarity scores before the softmax function. This dynamically re-weights how much attention each token pays to others. Like LLaMA-Adapter, it uses a zero-initialization (or "cold-start") strategy for stability.

  该图像是图10，展示了LLaMA-Excitor的结构示意图，包含可训练的Learnable Prompts，通过Excitor模块实现Key重建与Cold-Start Gating机制，并与注意力层中不同token的Softmax相加。

• Advantage: Extremely lightweight (even fewer parameters than LLaMA-Adapter V1, ~0.5M) and shown to be particularly effective for noisy instruction data and improving reasoning.

4.3.5 QLoRA (Quantized LoRA)

• Principle: A breakthrough technique that makes fine-tuning massive models feasible on consumer or prosumer-grade hardware. It combines LoRA with aggressive quantization.

• Mechanism:

  1. Quantization: The pre-trained LLaMA model's weights are quantized from 16-bit to an aggressive 4-bit precision using a novel format called NormalFloat4 (NF4).

  2. Frozen Base Model: The entire 4-bit base model is frozen and its weights are not updated during training.

  3. LoRA Adapters: Small LoRA adapters are inserted into the quantized model. Only these adapters are trained. The gradients are computed for the LoRA weights.

  4. Paged Optimizers: A memory-saving technique is used to handle optimizer states for very large models, preventing out-of-memory errors.

     该图像是一个对比示意图，展示了全量微调（无适配器）、LoRA和QLoRA三种微调方法在优化器状态、适配器和基础模型参数位数及参数更新流程的差异与流程。

• Advantage: Drastically reduces GPU memory usage. The authors note that QLoRA enabled fine-tuning a 65B LLaMA model on a single 48GB GPU, a task that would normally require multiple high-end GPUs.

  The paper provides a table (transcribed below) comparing the parameter and memory footprints of these methods.

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Table 2: Parameter count and memory footprints for tuning LLaMA-7B with various methods. (Manual Transcription)

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5. Experimental Setup

The paper is a survey, so it synthesizes results from multiple original research papers rather than conducting its own novel experiments.

• Datasets: The performance of LLaMA and its fine-tuned variants are evaluated on a wide range of standard benchmarks mentioned in the text:

  • MMLU (Massive Multitask Language Understanding): A diverse benchmark testing general knowledge and problem-solving ability across 57 subjects.
  • AlpacaEval: An evaluation framework that uses a powerful LLM (like GPT-4) to judge the quality of a model's responses to a set of instructions.
  • ScienceQA: A multimodal benchmark featuring scientific questions that may include text, diagrams, and other visual aids.
  • MSCOCO Captioning: A classic vision-language task where the model must generate a descriptive text caption for an image.
  • GSM8K: A dataset of grade-school math word problems used to evaluate arithmetic reasoning.

• Evaluation Metrics:

  • AUROC (Area Under the Receiver Operating Characteristic Curve):
    1. Conceptual Definition: A metric used for binary classification tasks. It measures the ability of a model to distinguish between positive and negative classes across all possible classification thresholds. An AUROC of 1.0 represents a perfect classifier, while 0.5 represents a model with no discriminative ability (random guessing).
    2. Mathematical Formula: $$\text{AUROC} = \int_{0}^{1} \text{TPR}(T) \, d(\text{FPR}(T))$$
    3. Symbol Explanation:
       • $\text{TPR}$ (True Positive Rate) = $\frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}}$
       • $\text{FPR}$ (False Positive Rate) = $\frac{\text{False Positives}}{\text{False Positives} + \text{True Negatives}}$
       • $T$ is the classification threshold. The integral is computed over all possible threshold values.
  • CIDEr (Consensus-based Image Description Evaluation):
    1. Conceptual Definition: A metric for evaluating the quality of generated image captions. It measures the consensus between a candidate caption and a set of human-written reference captions. It does this by treating each sentence as a "bag of words" (represented by TF-IDF vectors) and computing the average cosine similarity between the candidate and the references, with higher scores being better.
    2. Mathematical Formula: $$\text{CIDEr}_n(c_i, S_i) = \frac{1}{m} \sum_{j=1}^{m} \frac{g^n(c_i) \cdot g^n(s_{ij})}{||g^n(c_i)|| \cdot ||g^n(s_{ij})||}$$
    3. Symbol Explanation:
       • $c_i$ is the candidate caption for image $i$.
       • $S_i = \{s_{i1}, ..., s_{im}\}$ is the set of $m$ human reference captions for image $i$.
       • $g^n(\cdot)$ is a function that maps a sentence to a vector of TF-IDF weights for all its n-grams of length $n$.
       • The final CIDEr score is a weighted sum of scores for different n-gram lengths (e.g., n=1 to 4).

• Baselines: The primary baselines are the original, unfine-tuned LLaMA models. In many cases, performance is also compared against larger, proprietary models like GPT-3 and GPT-4, or against other open-source fine-tuned models like Alpaca and Vicuna.

6. Results & Analysis

The paper's analysis focuses on comparing the different PEFT methods and discussing the evolution of reasoning capabilities.

6.1 Meta-Analysis of PEFT Methods

The authors provide a meta-analysis in Table 3, which synthesizes the strengths, weaknesses, and typical use cases of each PEFT method. This table is a cornerstone of the paper's contribution.

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Table 3: Experimental Comparison of PEFT Methods for LLaMA Models. (Manual Transcription)

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Analysis:

• Efficiency vs. Capability Trade-off: The table clearly shows a trade-off. LLaMA-Excitor and LLaMA-Adapter V1 are the most parameter-frugal, making them ideal for rapid, low-resource tuning. LLaMA-Adapter V2, with more trainable parameters, offers superior performance, especially for complex multimodal tasks.
• Mergeability: A key practical distinction is whether the adapter can be merged back into the base model to eliminate inference overhead. LoRA, QLoRA, and LLaMA-Excitor are mergeable, while the LLaMA-Adapter methods are not, as they introduce structural changes (prompt tokens).
• Hardware Accessibility: QLoRA stands out as a game-changer for accessibility, enabling researchers and developers with limited hardware to work with state-of-the-art models (e.g., 65B scale).
• Task Specialization: The analysis suggests which tool to use for which job. For general domain adaptation, LoRA is a strong default. For multimodal instruction following, LLaMA-Adapter V2 is superior. For tuning on noisy data or enhancing reasoning, LLaMA-Excitor is a promising candidate.

6.2 Reasoning in the LLaMA Series

The paper dedicates a section to the evolution of reasoning techniques, which are crucial for solving complex problems.

该图像是一张示意图，展示了LLaMA模型的结构和多种推理增强方法的整体流程，包括任务输入、嵌入生成、自注意力机制及前馈网络等关键组件。

The authors trace the progression from simple prompting to more sophisticated frameworks.

该图像是图表，展示了LLaMA模型中推理机制的演变，从直接提示到链式推理、再到自一致性和思维图，分别以流程步骤和路径连接方式体现复杂推理过程。

• Chain-of-Thought (CoT): The model is prompted to "think step by step," generating intermediate reasoning steps before giving a final answer. This breaks down complex problems and has been shown to dramatically improve performance on arithmetic and logical tasks.

• Self-Consistency (CoT-SC): An enhancement over CoT where the model generates multiple reasoning paths and takes a majority vote on the final answer, improving robustness.

• Tree-of-Thoughts (ToT): A more advanced framework where the model explores multiple reasoning paths in a tree-like structure, allowing it to backtrack and self-correct.

• Graph-of-Thoughts (GoT): The most recent evolution, which models reasoning as a graph. This allows for more complex, non-linear thought processes where ideas can be merged and synthesized, outperforming linear or tree-based methods on complex planning tasks.

  The paper argues that PEFT methods are essential for teaching LLaMA models to effectively use these reasoning strategies for specific domains, and it provides a table summarizing the trade-offs.

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Table 4: Comparison of PEFT Methods: Accuracy Improvement vs. Parameter Efficiency (Manual Transcription)

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7. Applications of LLaMA and PEFT in Real-World Domains

The survey highlights how the combination of LLaMA models and PEFT has unlocked numerous practical applications by enabling cost-effective specialization.

该图像是图14，展示了通过参数高效微调（PEFT）技术增强的LLaMA模型在多个关键应用领域的分布和示意，涵盖法律、医疗等实际场景。

Key domains discussed include:

• Healthcare & Biomedicine: Fine-tuning LLaMA on medical texts for tasks like clinical text summarization, medical question answering, and predicting drug-disease interactions. LoRA has been shown to significantly improve performance on such specialized tasks.
• Legal Domain: Adapting LLaMA for contract analysis, legal research, and compliance monitoring.
• Low-Resource Languages: Using PEFT to adapt LLaMA to languages with little training data, democratizing access to powerful NLP tools.
• Domain-Specific Chatbots: Creating expert chatbots for finance, customer support, or education by fine-tuning LLaMA on domain-specific knowledge.
• Vision-and-Language: Using multimodal models like LLaMA-3.2 with methods like LLaMA-Adapter V2 for image captioning, visual question answering (VQA), and document understanding.

8. Conclusion & Reflections

• Conclusion Summary: The paper concludes that the rapid evolution of Meta's LLaMA models, combined with the development of increasingly sophisticated PEFT methods, has created a powerful and accessible ecosystem for AI research and development. The survey successfully consolidates this knowledge, providing a clear overview of the model lineage, the mechanisms of key PEFT techniques, and their practical applications. It serves its stated purpose as a valuable "one-stop resource."

• Limitations & Future Work: The authors point to several ongoing challenges:

  • Scaling to Larger Contexts: While LLaMA 4's 10M token context is impressive, effectively utilizing such vast contexts remains a research challenge.
  • Improving Robustness: Making models more robust to noisy data and adversarial attacks is a key area for future work.
  • Hybrid PEFT Methods: The paper suggests that future research will likely explore hybrid approaches that combine the efficiency of LoRA/QLoRA with the advanced capabilities of methods like LLaMA-Adapter V2.

• Personal Insights & Critique:

  • Excellent Synthesis: The paper's primary strength is its excellent synthesis of two intertwined but distinct topics: the evolution of a specific model family (LLaMA) and the fine-tuning techniques (PEFT) that grew alongside it. This focused approach provides more practical depth for a practitioner interested in LLaMA than a general LLM survey would.
  • Hypothetical Nature: The paper's futuristic perspective (written as if in late 2025) is a novel framing device. While it allows for a more complete "story" of LLaMA's evolution, it is based on speculation. A reader in the actual year 2024 would need to distinguish between the real, existing models (LLaMA 1-3) and the fictional ones (LLaMA 4).
  • Potential for Deeper Technical Analysis: While the explanations of the PEFT methods are clear, a deeper dive into the theoretical underpinnings (e.g., why low-rank updates work so well, the mathematical stability of zero-initialized gating) could have added further value for a research-oriented audience.
  • Structure: The inclusion of the "Reasoning" section (Section 6) feels slightly disjointed from the main narrative of LLaMA models and PEFT methods. While reasoning is a critical capability, its connection to the specific PEFT methods could have been integrated more tightly throughout the paper rather than being a separate section.
  • Overall Impact: Despite these minor critiques, the paper provides an outstanding and highly useful overview. Its clear structure, detailed tables, and beginner-friendly explanations of complex topics make it an ideal starting point for anyone looking to understand and apply LLaMA models and parameter-efficient fine-tuning.