Pragmatic Reasoning improves LLM Code Generation

• URL: http://arxiv.org/abs/2502.15835v2
• Date: Fri, 28 Feb 2025 13:40:42 GMT
• Title: Pragmatic Reasoning improves LLM Code Generation
• Authors: Zhuchen Cao, Sven Apel, Adish Singla, Vera Demberg,
• Abstract summary: We propose CodeRSA, a novel code candidate reranking mechanism built upon the Rational Speech Act (RSA) framework.<n>We evaluate CodeRSA using one of the latest Large Language Models on a popular code generation dataset.
• Score: 35.78260347663757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated impressive potential in translating natural language (NL) instructions into program code. However, user instructions often contain inherent ambiguities, making it challenging for LLMs to generate code that accurately reflects the user's true intent. To address this challenge, researchers have proposed to produce multiple candidates of the program code and then rerank them to identify the best solution. In this paper, we propose CodeRSA, a novel code candidate reranking mechanism built upon the Rational Speech Act (RSA) framework, designed to guide LLMs toward more comprehensive pragmatic reasoning about user intent. We evaluate CodeRSA using one of the latest LLMs on a popular code generation dataset. Our experiment results show that CodeRSA consistently outperforms common baselines, surpasses the state-of-the-art approach in most cases, and demonstrates robust overall performance. These findings underscore the effectiveness of integrating pragmatic reasoning into code candidate reranking, offering a promising direction for enhancing code generation quality in LLMs.

Related papers

• zsLLMCode: An Effective Approach for Code Embedding via LLM with Zero-Shot Learning [6.976968804436321]
  This paper proposes a novel zero-shot approach, zsLLMCode, to generate code embeddings by using large language models (LLMs) and sentence embedding models. The results have demonstrated the effectiveness and superiority of our method over state-of-the-art unsupervised approaches.
  arXiv  Detail & Related papers  (2024-09-23T01:03:15Z)
• EPiC: Cost-effective Search-based Prompt Engineering of LLMs for Code Generation [8.009881267479189]
  Large Language Models (LLMs) have seen increasing use in various software development tasks, especially in code generation. We propose an alternative approach named Evolutionary Prompt Engineering for Code (EPiC) to evolve the original prompts toward better ones that produce high-quality code. Our evaluation against state-of-the-art (SOTA) LLM-based code generation models shows that EPiC outperforms all the baselines in terms of cost-effectiveness.
  arXiv  Detail & Related papers  (2024-08-20T21:15:36Z)
• Source Code Summarization in the Era of Large Language Models [23.715005053430957]
  Large language models (LLMs) have led to a great boost in the performance of code-related tasks. In this paper, we undertake a systematic and comprehensive study on code summarization in the era of LLMs.
  arXiv  Detail & Related papers  (2024-07-09T05:48:42Z)
• What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
  Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions. We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types. We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
  arXiv  Detail & Related papers  (2024-07-08T17:27:17Z)
• A Survey on Large Language Models for Code Generation [9.555952109820392]
  Large Language Models (LLMs) have garnered remarkable advancements across diverse code-related tasks. This survey aims to bridge the gap between academia and practical development by providing a comprehensive and up-to-date literature review.
  arXiv  Detail & Related papers  (2024-06-01T17:48:15Z)
• 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 inserts 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)
• CodecLM: Aligning Language Models with Tailored Synthetic Data [51.59223474427153]
  We introduce CodecLM, a framework for adaptively generating high-quality synthetic data for instruction-following abilities. We first encode seed instructions into metadata, which are concise keywords generated on-the-fly to capture the target instruction distribution. We also introduce Self-Rubrics and Contrastive Filtering during decoding to tailor data-efficient samples.
  arXiv  Detail & Related papers  (2024-04-08T21:15:36Z)
• Reasoning Runtime Behavior of a Program with LLM: How Far Are We? [25.451857140926943]
  Large language models for code (i.e., code LLMs) have shown strong code understanding and generation capabilities. Code reasoning is one of the most essential abilities of code LLMs. We propose a framework, namely REval, for evaluating code reasoning abilities and consistency of code LLMs with program execution.
  arXiv  Detail & Related papers  (2024-03-25T05:37:16Z)
• InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models [56.723509505549536]
  InfiBench is the first large-scale freeform question-answering (QA) benchmark for code to our knowledge. It comprises 234 carefully selected high-quality Stack Overflow questions that span across 15 programming languages. We conduct a systematic evaluation for over 100 latest code LLMs on InfiBench, leading to a series of novel and insightful findings.
  arXiv  Detail & Related papers  (2024-03-11T02:06:30Z)
• 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)
• 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)
• Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach [12.214585409361126]
  Large language models (LLMs)- based code generation is a complex and powerful black-box model. We propose a novel causal graph-based representation of the prompt and the generated code. We illustrate the insights that our framework can provide by studying over 3 popular LLMs with over 12 prompt adjustment strategies.
  arXiv  Detail & Related papers  (2023-10-10T14:56:26Z)

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.