On the Impact of Fine-Tuning on Chain-of-Thought Reasoning (2411.15382v1)

Abstract: LLMs have emerged as powerful tools for general intelligence, showcasing advanced natural language processing capabilities that find applications across diverse domains. Despite their impressive performance, recent studies have highlighted the potential for significant enhancements in LLMs' task-specific performance through fine-tuning strategies like Reinforcement Learning with Human Feedback (RLHF), supervised fine-tuning (SFT), and Quantized Low-Rank Adapters (Q-LoRA) method. However, previous works have shown that while fine-tuning offers significant performance gains, it also leads to challenges such as catastrophic forgetting and privacy and safety risks. To this end, there has been little to no work in \textit{understanding the impact of fine-tuning on the reasoning capabilities of LLMs}. Our research investigates the effect of fine-tuning on the reasoning abilities of LLMs, addressing critical questions regarding the impact of task-specific fine-tuning on overall reasoning capabilities, the influence of fine-tuning on Chain-of-Thought (CoT) reasoning performance, and the implications for the faithfulness of CoT reasonings. By exploring these dimensions, our study shows the impact of fine-tuning on LLM reasoning capabilities, where the faithfulness of CoT reasoning, on average across four datasets, decreases, highlighting potential shifts in internal mechanisms of the LLMs resulting from fine-tuning processes.

Impact of Fine-Tuning on Chain-of-Thought Reasoning in LLMs

The paper "On the Impact of Fine-Tuning on Chain-of-Thought Reasoning" explores the nuanced effects of fine-tuning LLMs specifically focusing on alteration in reasoning capabilities. While LLMs like GPT-3.5 and GPT-4 are typically celebrated for their problem-solving skills enhanced by chain-of-thought (CoT) prompting, fine-tuning is often employed to improve their performance on domain-specific tasks. This research interrogates the broader implications of such fine-tuning efforts at the intersection of reasoning aptitude and task specialization.

Fine-Tuning Techniques and Methodology

The paper rigorously examines various fine-tuning strategies including Reinforcement Learning with Human Feedback (RLHF), supervised fine-tuning (SFT), and a resource-efficient Quantized Low-Rank Adapters (Q-LoRA) method. These methods modify pre-trained models to improve accuracy and relevance in specific domains such as medical reasoning and common-sense comprehension.

Focusing primarily on Q-LoRA due to its computational efficiency, the paper fine-tunes models with varying low-rank parameter configurations and assesses performance shifts in reasoning tasks. This involves evaluating changes in model response fidelity and accuracy across datasets like mathematical problem sets (GSM8K), medical exams (MedQA, MedMCQA), and common-sense reasoning assessments (CosmosQA).

Key Findings

1. Reasoning Performance Deterioration: The research shows that fine-tuning, especially on non-reasoning and common-sense datasets, tends to degrade the model's reasoning performance. This degradation in accuracy is markedly pronounced in smaller models like Llama-3-8B-Instruct compared to larger counterparts such as GPT-4.
2. Impact on Faithfulness of CoT Reasoning: The paper measures the faithfulness of generated reasoning steps to the model’s final answers using metrics like Early Termination, Paraphrasing, and Filler Substitution. Findings suggest that fine-tuning can lead to a decrease in the faithfulness of reasoning chains, thereby impacting the integrity of LLM-driven problem-solving processes.
3. Differential Impact on Model Sizes: Larger models exhibited more stable reasoning performance post fine-tuning, attributed to fewer perturbations in their parameter landscape during specialized task adjustments. This is contrasted with the lightweight Llama models, which exhibited notable reductions in reasoning reliability post-tuning on noncomplex datasets.
4. Trade-offs in Model Generalization: Fine-tuning enhances domain-specific performance but incurs a cost concerning general reasoning capabilities. The extent of this trade-off correlated significantly with model size and the complexity of the tuning task, showcasing a resilience in larger architectures.

Implications and Future Directions

The findings highlight the intrinsic trade-offs embedded in the fine-tuning process of LLMs. While fine-tuning can significantly enhance domain-specific task accuracy, there is a substantial risk of impairing broader reasoning capabilities. This pushes for a re-examination of how LLMs are adapted for specialization without sacrificing core reasoning competencies.

Future research is proposed to focus on advanced interpolation techniques that preserve reasoning integrity during fine-tuning. By investing in mechanisms such as Inference-Time Intervention (ITI) for real-time model adaptation insights and possibly developing new metrics for assessing reasoning fidelity post-fine-tuning, it can help align the fine-tuning processes with the preservation of reasoning fidelity. Furthermore, exploring these dynamics across a wider spectrum of reasoning and in-context prompting methods would provide a more comprehensive understanding of LLM adaptability.

This paper contributes a critical perspective to the ongoing discourse on model specialization through fine-tuning, underscoring the need for balanced approaches to extracting domain-specific improvements without losing general cognitive utility in LLMs.