Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMs

• URL: http://arxiv.org/abs/2403.13271v1
• Date: Wed, 20 Mar 2024 03:09:54 GMT
• Title: Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMs
• Authors: Zhihong Sun, Chen Lyu, Bolun Li, Yao Wan, Hongyu Zhang, Ge Li, Zhi Jin,
• Abstract summary: We propose the CodePLAN framework, which aims to transfer LLMs' code generation reasoning capabilities to smaller models. Our approach improves the smaller model's code generation performance by over 130% on the challenging APPS benchmark.
• Score: 36.409470894115074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have recently made significant advances in code generation through the 'Chain-of-Thought' prompting technique. This technique empowers the model to autonomously devise "solution plans" to tackle intricate programming challenges, thereby improving its performance in code generation. Nevertheless, smaller models have been struggling to keep up with LLMs in deducing these plans, adversely affecting their code generation capabilities. Given the considerable size and associated deployment costs, along with concerns about data security, many teams opt for deploying smaller models for code generation. Consequently, there arises a compelling need for transferring LLMs' code generation reasoning abilities to the smaller models. In this paper, we propose the CodePLAN framework, which aims to transfer LLMs' reasoning capabilities to smaller models through distillation. We adopt a multi-task learning approach, jointly undertaking code generation and solution plan generation tasks, to enhance the code generation capabilities of the smaller model. To ensure the superior quality of the solution plans, we advocate for the utilization of backward reasoning and plan sampling strategies. Our experiments show that in comparison to the conventional fine-tuning approach, our approach improves the smaller model's code generation performance (measured in pass@1 metric) by over 130% on the challenging APPS benchmark.

Related papers

• Non-myopic Generation of Language Models for Reasoning and Planning [45.75146679449453]
  This paper proposes a novel method, Predictive-Decoding, that leverages Model Predictive Control to enhance planning accuracy. Our experiments show significant improvements in a wide range of tasks for math, coding, and agents.
  arXiv  Detail & Related papers  (2024-10-22T17:13:38Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Contemporary Model Compression on Large Language Models Inference [7.307436175842646]
  Large Language Models (LLMs) have revolutionized natural language processing by achieving state-of-the-art results across a variety of tasks. The computational demands of LLM inference, including high memory consumption and slow processing speeds, pose significant challenges for real-world applications. This survey explores techniques in model compression that address these challenges by reducing the size and computational requirements of LLMs.
  arXiv  Detail & Related papers  (2024-09-03T15:35:01Z)
• Adaptive Draft-Verification for Efficient Large Language Model Decoding [24.347886232342862]
  Large language model (LLM) decoding involves generating a sequence of tokens based on a given context. The typical autoregressive decoding method requires a separate forward pass through the model for each token generated. We introduce ADED, which accelerates LLM decoding without requiring fine-tuning.
  arXiv  Detail & Related papers  (2024-06-27T22:20:39Z)
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback [58.20547418182074]
  We introduce StepCoder, a novel framework for code generation, consisting of two main components. CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks. FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization. Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
  arXiv  Detail & Related papers  (2024-02-02T13:14:31Z)
• LLM-Assisted Code Cleaning For Training Accurate Code Generators [53.087019724256606]
  We investigate data quality for code and find that making the code more structured and readable leads to improved code generation performance of the system. We build a novel data-cleaning pipeline that uses these principles to transform existing programs. We evaluate our approach on two challenging algorithmic code generation benchmarks and find that fine-tuning CodeLLaMa-7B improves the performance by up to 30% compared to fine-tuning on the original dataset.
  arXiv  Detail & Related papers  (2023-11-25T02:45:50Z)
• CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules [51.82044734879657]
  We propose CodeChain, a novel framework for inference that elicits modularized code generation through a chain of self-revisions. We find that CodeChain can significantly boost both modularity as well as correctness of the generated solutions, achieving relative pass@1 improvements of 35% on APPS and 76% on CodeContests.
  arXiv  Detail & Related papers  (2023-10-13T10:17:48Z)
• CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning [92.36705236706678]
  "CodeRL" is a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning. During inference, we introduce a new generation procedure with a critical sampling strategy. For the model backbones, we extended the encoder-decoder architecture of CodeT5 with enhanced learning objectives.
  arXiv  Detail & Related papers  (2022-07-05T02:42:15Z)

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.