Empirical Evaluation of Parameter-Efficient Fine-Tuning Methods for Large Language Models on Code Summarization and Generation Tasks, Including Knowledge Transfer to the Unseen Programming Language R

Core Concepts

This study empirically evaluates the effectiveness of parameter-efficient fine-tuning (PEFT) methods, specifically LoRA and Compacter, on large language models (LLMs) for code summarization and code generation tasks. It also investigates the capability of these PEFT methods in transferring knowledge from natural language LLMs to code-related tasks, and their ability to adapt the learned knowledge to an unseen programming language, R.

Abstract

This study aims to investigate the effectiveness of parameter-efficient fine-tuning (PEFT) methods for adapting large language models (LLMs) to code-related tasks. The researchers will focus on two PEFT methods, LoRA and Compacter, and evaluate their performance on two code intelligence tasks - code summarization and code generation.
The study will be conducted in three parts:

Effectiveness of PEFT methods on code-LLMs:

The researchers will fine-tune CodeT5 and CodeLlama models with and without LoRA and Compacter for code summarization and code generation tasks.
They will compare the performance of the PEFT-augmented models against the fully fine-tuned models.

Knowledge transfer from natural language LLMs to code tasks:

The researchers will use T5 and Llama 2 models (not pre-trained on code) and apply LoRA and Compacter to evaluate their ability to transfer knowledge from natural language to code-related tasks.
They will compare the results with the findings from the first part.

Adapting LLMs to an unseen programming language (R):

The researchers will assess the capability of the PEFT methods in adapting the learned knowledge of the code-LLMs to the unseen programming language R.
They will conduct experiments in two scenarios: 1) when R is entirely excluded during training, and 2) when R is introduced only during the fine-tuning phase.

The researchers will use standard evaluation metrics such as BLEU, CodeBLEU, and Pass@k to assess the performance of the models. They will also conduct human evaluations and additional experiments to further analyze the results.
The findings of this study will provide valuable insights into the capabilities of PEFT methods for adapting LLMs to code-related tasks, their ability to transfer knowledge from natural language to code, and their effectiveness in adapting to an unseen programming language. This can help make LLMs more accessible in scenarios with limited computational resources and enable the automation of software engineering tasks for a wider community of developers.

Stats

CodeT5 and CodeLlama are large language models with billions of parameters.
The CodeSearchNet dataset contains 2,326,976 functions paired with explanation comments in 6 programming languages.
The CoNaLa dataset contains 2,379 training samples and 500 test samples of Python code snippets and natural language intents.
The R dataset contains 10,353 code and comment pairs collected from GitHub repositories.
The HumanEvalR benchmark will be created by replicating the HumanEval dataset in the R programming language.

Quotes

None.

Key Insights Distilled From

Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to R

by Amirreza Esm... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01553.pdf

Deeper Inquiries

Question 1

PEFT methods have shown promising results in various code intelligence tasks beyond summarization and generation. When applied to tasks like code translation, code refactoring, or code vulnerability detection, these methods have demonstrated improved performance and efficiency. For code translation, PEFT methods can help in transferring knowledge from one programming language to another, enabling the model to understand and generate code in different languages. In code refactoring, PEFT methods can assist in restructuring and optimizing existing codebases by fine-tuning specific parts of the model related to refactoring patterns and best practices. For code vulnerability detection, PEFT methods can enhance the model's ability to identify and mitigate security vulnerabilities in code by focusing on relevant features and patterns associated with vulnerabilities.

Question 2

There are potential limitations and drawbacks when using PEFT methods for adapting LLMs to unseen programming languages. One limitation is the need for a sufficient amount of high-quality training data in the unseen language to effectively fine-tune the model. If the training data is limited or of poor quality, the model may not generalize well to the unseen language. Another limitation is the risk of overfitting to the training data, especially in low-resource settings where the model may not have enough diverse examples to learn from. To address these limitations, researchers can explore techniques like data augmentation, transfer learning from related languages, or incorporating domain-specific knowledge to improve the model's performance on unseen languages.

Question 3

The insights from studying PEFT methods can be extended to explore the adaptation of LLMs to other low-resource domains beyond programming languages. For example, in domain-specific terminologies or specialized technical fields, PEFT methods can be used to fine-tune LLMs on domain-specific datasets to improve the model's understanding and generation of specialized terminology or technical content. By leveraging PEFT methods, researchers can enhance the model's performance in low-resource domains by focusing on specific aspects of the domain and transferring knowledge efficiently. Additionally, exploring the adaptation of LLMs to other low-resource domains can open up opportunities for automating tasks in specialized fields such as healthcare, finance, or legal domains.