Applying RLAIF for Code Generation with API-usage in Lightweight LLMs

• URL: http://arxiv.org/abs/2406.20060v1
• Date: Fri, 28 Jun 2024 17:16:03 GMT
• Title: Applying RLAIF for Code Generation with API-usage in Lightweight LLMs
• Authors: Sujan Dutta, Sayantan Mahinder, Raviteja Anantha, Bortik Bandyopadhyay,
• Abstract summary: Reinforcement Learning from AI Feedback (RLAIF) has demonstrated significant potential across various domains. This paper introduces an RLAIF framework for improving the code generation abilities of lightweight (1B parameters) LLMs.
• Score: 15.366324461797582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning from AI Feedback (RLAIF) has demonstrated significant potential across various domains, including mitigating harm in LLM outputs, enhancing text summarization, and mathematical reasoning. This paper introduces an RLAIF framework for improving the code generation abilities of lightweight (<1B parameters) LLMs. We specifically focus on code generation tasks that require writing appropriate API calls, which is challenging due to the well-known issue of hallucination in LLMs. Our framework extracts AI feedback from a larger LLM (e.g., GPT-3.5) through a specialized prompting strategy and uses this data to train a reward model towards better alignment from smaller LLMs. We run our experiments on the Gorilla dataset and meticulously assess the quality of the model-generated code across various metrics, including AST, ROUGE, and Code-BLEU, and develop a pipeline to compute its executability rate accurately. Our approach significantly enhances the fine-tuned LLM baseline's performance, achieving a 4.5% improvement in executability rate. Notably, a smaller LLM model (780M parameters) trained with RLAIF surpasses a much larger fine-tuned baseline with 7B parameters, achieving a 1.0% higher code executability rate.

Related papers

• RIDE: Enhancing Large Language Model Alignment through Restyled In-Context Learning Demonstration Exemplars [57.6513924960128]
  Alignment tuning is crucial for ensuring large language models (LLMs) behave ethically and helpfully. This paper proposes a low-cost, tuning-free method using in-context learning (ICL) to enhance LLM alignment.
  arXiv  Detail & Related papers  (2025-02-17T11:16:19Z)
• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression. LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model. Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• Improving the Ability of Pre-trained Language Model by Imparting Large Language Model's Experience [4.814313782484443]
  Large Language Models (LLMs) and pre-trained Language Models (LMs) have achieved impressive success on many software engineering tasks. We use LLMs to generate domain-specific data, thereby improving the performance of pre-trained LMs on the target tasks.
  arXiv  Detail & Related papers  (2024-08-16T06:37:59Z)
• A Performance Study of LLM-Generated Code on Leetcode [1.747820331822631]
  This study evaluates the efficiency of code generation by Large Language Models (LLMs) We compare 18 LLMs, considering factors such as model temperature and success rate, and their impact on code performance. We find that LLMs are capable of generating code that is, on average, more efficient than the code written by humans.
  arXiv  Detail & Related papers  (2024-07-31T13:10:03Z)
• LeDex: Training LLMs to Better Self-Debug and Explain Code [36.604898865514365]
  LeDex is a training framework that significantly improves the self-ging capability of LLMs. We propose an automated pipeline to collect a high-quality dataset for code explanation and refinement. We perform supervised fine-tuning (SFT) and further reinforcement learning (RL) on both success and failure trajectories with a novel reward design.
  arXiv  Detail & Related papers  (2024-05-28T23:20:24Z)
• LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement [79.31084387589968]
  Pretrained large language models (LLMs) are currently state-of-the-art for solving the vast majority of natural language processing tasks. We propose LLM2LLM, a data augmentation strategy that uses a teacher LLM to enhance a small seed dataset. We achieve improvements up to 24.2% on the GSM8K dataset, 32.6% on CaseHOLD, 32.0% on SNIPS, 52.6% on TREC and 39.8% on SST-2 over regular fine-tuning in the low-data regime.
  arXiv  Detail & Related papers  (2024-03-22T08:57:07Z)
• CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences [5.165576022684194]
  We propose using the LLM-as-a-Judge methodology to evaluate the alignment of LLMs with coding preferences. CodeUltraFeedback consists of 10,000 coding instructions, each annotated with four responses generated from a diverse pool of 14 LLMs. In turn, we explore the usage of CodeUltraFeedback as feedback data to fine-tune and align CodeLlama-7B-Instruct using supervised fine-tuning (SFT) and reinforcement learning from AI feedback (RLAIF) with direct preference optimization (DPO)
  arXiv  Detail & Related papers  (2024-03-14T01:51:35Z)
• How Can LLM Guide RL? A Value-Based Approach [68.55316627400683]
  Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. Recent developments in large language models (LLMs) have showcased impressive capabilities in language understanding and generation, yet they fall short in exploration and self-improvement capabilities. We develop an algorithm named LINVIT that incorporates LLM guidance as a regularization factor in value-based RL, leading to significant reductions in the amount of data needed for learning.
  arXiv  Detail & Related papers  (2024-02-25T20:07:13Z)
• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models [52.98743860365194]
  We propose a new fine-tuning method called Self-Play fIne-tuNing (SPIN) At the heart of SPIN lies a self-play mechanism, where the LLM refines its capability by playing against instances of itself. This sheds light on the promise of self-play, enabling the achievement of human-level performance in LLMs without the need for expert opponents.
  arXiv  Detail & Related papers  (2024-01-02T18:53:13Z)
• Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models [11.845239346943067]
  parameter-efficient fine-tuning (PEFT) is a promising approach to efficiently specialize large language models (LLMs) to task-specific data. Our study highlights the potential for tuning larger LLMs and significant reductions in memory usage by combining PEFT with quantization.
  arXiv  Detail & Related papers  (2023-08-21T04:31:06Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.