Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models (2308.10462v2)

Abstract: LLMs demonstrate impressive capabilities to generate accurate code snippets given natural language intents in zero-shot, i.e., without the need for specific fine-tuning. While prior studies have highlighted the advantages of fine-tuning LLMs, this process incurs high computational costs, making it impractical in resource-scarce environments, particularly for models with billions of parameters. To address these challenges, previous research explored In-Context Learning (ICL) as a strategy to guide the LLM generative process with task-specific prompt examples. However, ICL introduces inconveniences, such as the need for designing contextually relevant prompts and the absence of learning task-specific parameters, thereby limiting downstream task performance. In this context, we foresee Parameter-Efficient Fine-Tuning (PEFT) techniques as a promising approach to efficiently specialize LLMs to task-specific data while maintaining reasonable resource consumption. In this paper, we deliver a comprehensive study of PEFT techniques for LLMs under the automated code generation scenario. Our comprehensive investigation of PEFT techniques for LLMs reveals their superiority and potential over ICL across a diverse set of LLMs. Additionally, we demonstrate the extended capabilities of PEFT, showcasing its ability to learn from two distinct datasets jointly without compromising performance. Furthermore, our study highlights the potential for tuning larger LLMs and significant reductions in memory usage by combining PEFT with quantization. Therefore, this study opens opportunities for broader applications of PEFT in software engineering scenarios. Our code is available at https://github.com/martin-wey/peft-LLM-code/.

Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with LLMs

The paper "Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with LLMs" systematically examines the application of parameter-efficient fine-tuning (PEFT) techniques to LLMs in the context of automated code generation. The paper is motivated by the computational inefficiencies associated with traditional full fine-tuning and the practical limitations of In-Context Learning (ICL) regarding resource constraints and contextual examples, specifically when dealing with LLMs exceeding 1 billion parameters.

Research Focus and Methodology

The research compares multiple tuning strategies, including full fine-tuning for small LLMs, ICL, and several PEFT techniques—LoRA, IA3, Prompt tuning, Prefix tuning, and QLoRA—across a diverse range of LLMs. The paper aims to identify the efficacy of PEFT techniques by addressing specific research questions regarding their comparative performance against both smaller models and ICL, the practical feasibility of PEFT in resource-limited settings, the potential of joint training on multiple datasets, and the effects of incorporating quantization to reduce resource usage further.

Key Findings

1. Performance Analysis:
   • LLMs fine-tuned with PEFT, particularly LoRA, consistently outperform smaller models fine-tuned traditionally, highlighting a performance increase up to 72.3% in terms of EM@k metrics.
   • LoRA emerges as the most effective PEFT method overall, consistently outperforming others like IA3 and Prefix tuning across different model sizes and datasets.
2. PEFT vs. ICL:
   • PEFT methods demonstrate superior performance relative to ICL, yielding improvements of 22.8% to 150% in evaluation metrics across tested datasets. This underscores the value of PEFT for situations where precise task-specific adaptation is required.
3. Resource Efficiency:
   • The application of QLoRA showcases that quantization strategies can significantly cut memory usage while improving or maintaining performance, enabling the fine-tuning of models with up to 34 billion parameters within a constrained computational environment.
4. Joint Training Capabilities:
   • The investigation into the joint training of LLMs on multiple datasets revealed no significant loss of performance when utilizing a single LoRA adapter across tasks, suggesting that PEFT techniques support flexible model adaptation in multi-task scenarios.

Implications and Future Research Directions

The presented paper's findings underscore the significance of PEFT as a robust strategy for enhancing the adaptability of LLMs in practical code generation applications, particularly within limited-resource environments. The demonstrated efficiency gains and computational reductions position PEFT as a potentially transformative approach for software engineering tasks requiring nuanced LLM adaptations without the prohibitive costs associated with full parameter updates.

The research opens several directions for future exploration, including expanding the domain applications of PEFT beyond code generation to other complex software engineering tasks and considering its integration within continual learning frameworks. Moreover, exploring hybrid approaches that incorporate both PEFT and advanced retrieval methods for ICL might uncover further potential efficiencies in leveraging large models for dynamic and scalable code-related tasks.