Uncertainty-Guided Chain-of-Thought for Code Generation with LLMs

• URL: http://arxiv.org/abs/2503.15341v1
• Date: Wed, 19 Mar 2025 15:40:45 GMT
• Title: Uncertainty-Guided Chain-of-Thought for Code Generation with LLMs
• Abstract summary: Chain-of-Thought (CoT) reasoning has been demonstrated as an effective technique for improving the problem-solving capabilities of large language models (LLMs)<n>We introduce UnCert-CoT, an approach designed to enhance code generation by incorporating an uncertainty-aware CoT reasoning mechanism.
• Score: 45.33160999781074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chain-of-Thought (CoT) reasoning has been demonstrated as an effective technique for improving the problem-solving capabilities of large language models (LLMs) in the context of code generation. However, existing CoT methods often exhibit a tendency toward "overthinking", where the LLM consistently applies reasoning strategies without adequately considering the task's underlying complexity. This results in the LLMs allocating excessive computational resources, in terms of tokens, to relatively simple tasks or problems where the correct answer is already evident. Additionally, this overthinking may lead LLMs down incorrect reasoning paths, resulting in incorrect code generation. In this paper, we introduce UnCertainty-Aware Chain-of-Thought (UnCert-CoT), an LLM-based approach designed to enhance code generation by incorporating an uncertainty-aware CoT reasoning mechanism, which focuses computational resources on targeting points where LLMs are more prone to error. We propose two confidence-based uncertainty measures: Entropy-based and Probability Differential-based methods. When uncertainty is high, UnCert-CoT activates CoT-decoding to generate multiple reasoning paths and selects the final code that exhibits the highest likelihood of correctness. In contrast, LLM directly generates the code when uncertainty is low. This uncertainty judgment mechanism allows LLMs to prioritize complex tasks and avoid unnecessary steps in simpler cases, thereby improving overall efficiency and accuracy in code generation. Our experimental results demonstrate that UnCert-CoT significantly enhances code generation accuracy on challenging benchmark MHPP(Mostly Hard Python Problems), it achieves improvements up to 6.1% on PassRate accuracy, particularly in situations where traditional LLMs are prone to errors.

Related papers

• CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery [51.07538881798502]
  Large language models (LLMs) offer a promising source of domain knowledge to complement statistical inference.<n>We propose CauTion, a framework that reliably integrates LLM domain knowledge into an ensemble of statistical causal discovery algorithms.<n>CauTion consistently outperforms both data-centric and LLM-augmented baselines.
  arXiv  Detail & Related papers  (2026-06-02T13:07:43Z)
• Inferring Code Correctness from Specification [0.0]
  Large language models (LLMs) have become integral to modern software development, enabling automated code generation at scale.<n>We propose TRAILS (Targeted Reasoning Agreement via Inputs and Specifications), an approach that grounds LLM reasoning with concrete (input, output) pairs.<n>We evaluate TRAILS on two datasets, LiveCodeBench and CoCoClaNeL, across three LLMs (Qwen3Coder-30B, Devstral-Small-24B, and Olmo3.1-Instruct), comparing against HoarePrompt and a Zero-Shot Chain-of-Thought baseline.
  arXiv  Detail & Related papers  (2026-05-28T12:04:51Z)
• CodeSimpleQA: Scaling Factuality in Code Large Language Models [55.705748501461294]
  We present CodeSimpleQA, a comprehensive benchmark designed to evaluate the factual accuracy of code LLMs in answering code-related questions.<n>We also create CodeSimpleQA-Instruct, a large-scale instruction corpus with 66M samples, and develop a post-training framework combining supervised fine-tuning and reinforcement learning.
  arXiv  Detail & Related papers  (2025-12-22T14:27:17Z)
• Reasoning with Confidence: Efficient Verification of LLM Reasoning Steps via Uncertainty Heads [104.9566359759396]
  We propose a lightweight alternative for step-level reasoning verification based on data-driven uncertainty scores.<n>Our findings suggest that the internal states of LLMs encode their uncertainty and can serve as reliable signals for reasoning verification.
  arXiv  Detail & Related papers  (2025-11-09T03:38:29Z)
• Can LLMs Correct Themselves? A Benchmark of Self-Correction in LLMs [57.10533368622962]
  Self-correction of large language models (LLMs) emerges as a critical component for enhancing their reasoning performance.<n>This study introduces CorrectBench, a benchmark developed to evaluate the effectiveness of self-correction strategies.<n>Our findings reveal that: 1) Self-correction methods can improve accuracy, especially for complex reasoning tasks; 2) Mixing different self-correction strategies yields further improvements, though it reduces efficiency; and 3) Reasoning LLMs (e.g., DeepSeek-R1) have limited optimization under additional self-correction methods and have high time costs.
  arXiv  Detail & Related papers  (2025-10-17T02:40:19Z)
• Uncovering Systematic Failures of LLMs in Verifying Code Against Natural Language Specifications [0.6813925418351435]
  Large language models (LLMs) have become essential tools in software development, widely used for requirements engineering, code generation and review tasks.<n>In this paper, we uncover a systematic failure of LLMs in evaluating whether code aligns with natural language requirements.<n>Our results reveal that LLMs frequently misclassify correct code implementations as either not satisfying requirements'' or containing potential defects.
  arXiv  Detail & Related papers  (2025-08-17T13:07:26Z)
• R-Stitch: Dynamic Trajectory Stitching for Efficient Reasoning [60.37610817226533]
  Chain-of-thought (CoT) reasoning encourages step-by-step intermediate reasoning during inference.<n>CoT introduces substantial computational overhead due to its reliance on autoregressive decoding over long token sequences.<n>We present R-Stitch, a token-level, confidence-based hybrid decoding framework that accelerates CoT inference.
  arXiv  Detail & Related papers  (2025-07-23T08:14:36Z)
• Code Execution as Grounded Supervision for LLM Reasoning [36.97199200274124]
  Training large language models with chain-of-thought (CoT) supervision has proven effective for enhancing their reasoning abilities.<n>We propose a scalable method for generating a high-quality CoT supervision dataset by leveraging the determinism of program execution.<n>Our approach extracts verifiable, step-by-step reasoning traces from code execution and transforms them into a natural language CoT reasoning.
  arXiv  Detail & Related papers  (2025-06-12T04:36:57Z)
• SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs [48.28847964704554]
  Chain-of-Thought (CoT) reasoning enables Large Language Models (LLMs) to solve complex reasoning tasks.<n>We propose a novel approach for continuous-space reasoning that does not require modifying the underlying LLM.
  arXiv  Detail & Related papers  (2025-02-17T18:52:29Z)
• Assessing Correctness in LLM-Based Code Generation via Uncertainty Estimation [0.0]
  We explore uncertainty estimation as a proxy for correctness in LLM-generated code. We adapt two state-of-the-art techniques from natural language generation to the domain of code generation. Our findings indicate a strong correlation between the uncertainty computed through these techniques and correctness.
  arXiv  Detail & Related papers  (2025-02-17T10:03:01Z)
• ROCODE: Integrating Backtracking Mechanism and Program Analysis in Large Language Models for Code Generation [31.363781211927947]
  Large language models (LLMs) have achieved impressive performance in code generation. LLMs are susceptible to error accumulation during code generation. We propose ROCODE, which integrates the backtracking mechanism and program analysis into LLMs for code generation.
  arXiv  Detail & Related papers  (2024-11-11T16:39:13Z)
• Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification [52.095460362197336]
  Large language models (LLMs) struggle with consistent and accurate reasoning. LLMs are trained primarily on correct solutions, reducing their ability to detect and learn from errors. We propose a novel collaborative method integrating Chain-of-Thought (CoT) and Program-of-Thought (PoT) solutions for verification.
  arXiv  Detail & Related papers  (2024-10-05T05:21:48Z)
• Understanding Defects in Generated Codes by Language Models [0.669087470775851]
  This study categorizes and analyzes 367 identified defects from code snippets generated by Large Language Models. Error categories indicate key areas where LLMs frequently fail, underscoring the need for targeted improvements. This paper implemented five prompt engineering techniques, including Scratchpad Prompting, Program of Thoughts Prompting, Chain-of-Thought Prompting, Chain-of-Thought Prompting, and Structured Chain-of-Thought Prompting.
  arXiv  Detail & Related papers  (2024-08-23T21:10:09Z)
• 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)
• Decoding with Limited Teacher Supervision Requires Understanding When to Trust the Teacher [11.136112399898481]
  How can small-scale large language models (LLMs) efficiently utilize the supervision of LLMs to improve their generative quality? We develop an algorithm to effectively aggregate the small-scale LLM and LLM predictions on initial tokens. We demonstrate that our method provides a consistent improvement over conventional decoding strategies.
  arXiv  Detail & Related papers  (2024-06-26T01:16:12Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• Fixing Large Language Models' Specification Misunderstanding for Better Code Generation [13.494822086550604]
  muFiX is a novel prompting technique to improve the code generation performance of large language models (LLMs)<n>It first exploits test case analysis to obtain specification understanding and enables a self-improvement process.<n>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)
• RCOT: Detecting and Rectifying Factual Inconsistency in Reasoning by Reversing Chain-of-Thought [56.558892336235914]
  Reversing Chain-of-Thought (RCoT) is a novel method to improve large language models' reasoning abilities. RCoT automatically detects and rectifys factual inconsistency in generated solutions. We show that manually written fine-grained feedback can dramatically improve LLMs' reasoning abilities.
  arXiv  Detail & Related papers  (2023-05-19T08:02:52Z)

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.