Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation

• URL: http://arxiv.org/abs/2310.10698v2
• Date: Sun, 22 Oct 2023 10:21:12 GMT
• Title: Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation
• Authors: Yingwei Ma, Yue Yu, Shanshan Li, Yu Jiang, Yong Guo, Yuanliang Zhang, Yutao Xie, Xiangke Liao
• Abstract summary: This paper proposes the "Semantic Chain-of-Thought" approach to intruduce semantic information of code, named SeCoT. We show that SeCoT can achieves state-of-the-art performance, greatly improving the potential for large models and code generation.
• Score: 22.219645213202178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have showcased remarkable prowess in code generation. However, automated code generation is still challenging since it requires a high-level semantic mapping between natural language requirements and codes. Most existing LLMs-based approaches for code generation rely on decoder-only causal language models often treate codes merely as plain text tokens, i.e., feeding the requirements as a prompt input, and outputing code as flat sequence of tokens, potentially missing the rich semantic features inherent in source code. To bridge this gap, this paper proposes the "Semantic Chain-of-Thought" approach to intruduce semantic information of code, named SeCoT. Our motivation is that the semantic information of the source code (\eg data flow and control flow) describes more precise program execution behavior, intention and function. By guiding LLM consider and integrate semantic information, we can achieve a more granular understanding and representation of code, enhancing code generation accuracy. Meanwhile, while traditional techniques leveraging such semantic information require complex static or dynamic code analysis to obtain features such as data flow and control flow, SeCoT demonstrates that this process can be fully automated via the intrinsic capabilities of LLMs (i.e., in-context learning), while being generalizable and applicable to challenging domains. While SeCoT can be applied with different LLMs, this paper focuses on the powerful GPT-style models: ChatGPT(close-source model) and WizardCoder(open-source model). The experimental study on three popular DL benchmarks (i.e., HumanEval, HumanEval-ET and MBPP) shows that SeCoT can achieves state-of-the-art performance, greatly improving the potential for large models and code generation.

Related papers

• OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models [70.72097493954067]
  Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning, tasks and agent systems. We introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an open cookbook'' for the research community.
  arXiv  Detail & Related papers  (2024-11-07T17:47:25Z)
• RGD: Multi-LLM Based Agent Debugger via Refinement and Generation Guidance [0.6062751776009752]
  Large Language Models (LLMs) have shown incredible potential in code generation tasks. LLMs can generate code based on task descriptions, but accuracy remains limited. We introduce a novel architecture of LLM-based agents for code generation and automatic debug: Refinement and Guidance debugger (RGD) RGD decomposes the code generation task into multiple steps, ensuring a clearer workflow and enabling iterative code refinement based on self-reflection and feedback.
  arXiv  Detail & Related papers  (2024-10-02T05:07:02Z)
• An Empirical Study on Capability of Large Language Models in Understanding Code Semantics [4.638578225024275]
  Large Language Models for Code (code LLMs) have demonstrated remarkable performance across various software engineering (SE) tasks. This paper introduces EMPICA, a framework designed to evaluate the capabilities of code LLMs in understanding code semantics.
  arXiv  Detail & Related papers  (2024-07-04T03:40:58Z)
• Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMs [57.27982780697922]
  Large language models have demonstrated exceptional capability in natural language understanding and generation. However, their generation speed is limited by the inherently sequential nature of their decoding process. This paper introduces Lexical Unit Decoding, a novel decoding methodology implemented in a data-driven manner.
  arXiv  Detail & Related papers  (2024-05-24T04:35:13Z)
• CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation [58.84212778960507]
  We propose CodeGRAG, a Graphical Retrieval Augmented Code Generation framework to enhance the performance of LLMs. CodeGRAG builds the graphical view of code blocks based on the control flow and data flow of them to fill the gap between programming languages and natural language. Various experiments and ablations are done on four datasets including both the C++ and python languages to validate the hard meta-graph prompt, the soft prompting technique, and the effectiveness of the objectives for pretrained GNN expert.
  arXiv  Detail & Related papers  (2024-05-03T02:48:55Z)
• CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code [56.019447113206006]
  Large Language Models (LLMs) have achieved remarkable progress in code generation. CodeIP is a novel multi-bit watermarking technique that embeds additional information to preserve provenance details. Experiments conducted on a real-world dataset across five programming languages demonstrate the effectiveness of CodeIP.
  arXiv  Detail & Related papers  (2024-04-24T04:25:04Z)
• Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective [85.48043537327258]
  We propose MANGO (comMents As Natural loGic pivOts), including a comment contrastive training strategy and a corresponding logical comment decoding strategy. Results indicate that MANGO significantly improves the code pass rate based on the strong baselines. The robustness of the logical comment decoding strategy is notably higher than the Chain-of-thoughts prompting.
  arXiv  Detail & Related papers  (2024-04-11T08:30:46Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• Exploring Large Language Models for Code Explanation [3.2570216147409514]
  Large Language Models (LLMs) have made remarkable strides in Natural Language Processing. This study specifically delves into the task of generating natural-language summaries for code snippets, using various LLMs.
  arXiv  Detail & Related papers  (2023-10-25T14:38:40Z)
• Test-Case-Driven Programming Understanding in Large Language Models for Better Code Generation [15.166827643436346]
  muFiX is a novel prompting technique to improve the code generation performance of large language models (LLMs) It first exploits test case analysis to obtain specification understanding and enables a self-improvement process. muFiX further fixes the specification understanding towards the direction reducing the gap between the provided understanding and the actual understanding.
  arXiv  Detail & Related papers  (2023-09-28T02:58:07Z)

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.