LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation

• URL: http://arxiv.org/abs/2409.20550v1
• Date: Mon, 30 Sep 2024 17:51:15 GMT
• Title: LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation
• Authors: Ziyao Zhang, Yanlin Wang, Chong Wang, Jiachi Chen, Zibin Zheng,
• Abstract summary: Code generation aims to automatically generate code from input requirements, significantly enhancing development efficiency. Recent large language models (LLMs) based approaches have shown promising results and revolutionized code generation task. Despite the promising performance, LLMs often generate contents with hallucinations, especially for the code generation scenario.
• Score: 33.46342144822026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code generation aims to automatically generate code from input requirements, significantly enhancing development efficiency. Recent large language models (LLMs) based approaches have shown promising results and revolutionized code generation task. Despite the promising performance, LLMs often generate contents with hallucinations, especially for the code generation scenario requiring the handling of complex contextual dependencies in practical development process. Although previous study has analyzed hallucinations in LLM-powered code generation, the study is limited to standalone function generation. In this paper, we conduct an empirical study to study the phenomena, mechanism, and mitigation of LLM hallucinations within more practical and complex development contexts in repository-level generation scenario. First, we manually examine the code generation results from six mainstream LLMs to establish a hallucination taxonomy of LLM-generated code. Next, we elaborate on the phenomenon of hallucinations, analyze their distribution across different models. We then analyze causes of hallucinations and identify four potential factors contributing to hallucinations. Finally, we propose an RAG-based mitigation method, which demonstrates consistent effectiveness in all studied LLMs. The replication package including code, data, and experimental results is available at https://github.com/DeepSoftwareAnalytics/LLMCodingHallucination

Related papers

• Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models [0.0]
  Large Language Models (LLMs) are powerful computational models trained on extensive corpora of human-readable text, enabling them to perform general-purpose language understanding and generation. Despite these successes, LLMs often produce inaccuracies, commonly referred to as hallucinations. This paper provides an empirical evaluation of different prompting strategies and frameworks aimed at reducing hallucinations in LLMs.
  arXiv  Detail & Related papers  (2024-10-25T08:34:53Z)
• MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation [50.73561815838431]
  Multimodal Large Language Models (MLLMs) frequently exhibit hallucination phenomena. We propose a novel dynamic correction decoding method for MLLMs (DeCo) We evaluate DeCo on widely-used benchmarks, demonstrating that it can reduce hallucination rates by a large margin compared to baselines.
  arXiv  Detail & Related papers  (2024-10-15T16:57:44Z)
• Collu-Bench: A Benchmark for Predicting Language Model Hallucinations in Code [20.736888384234273]
  We introduce Collu-Bench, a benchmark for predicting code hallucinations of large language models (LLMs) Collu-Bench includes 13,234 code hallucination instances collected from five datasets and 11 diverse LLMs, ranging from open-source models to commercial ones. We conduct experiments to predict hallucination on Collu-Bench, using both traditional machine learning techniques and neural networks, which achieves 22.03 -- 33.15% accuracy.
  arXiv  Detail & Related papers  (2024-10-13T20:41:47Z)
• LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models [96.64960606650115]
  LongHalQA is an LLM-free hallucination benchmark that comprises 6K long and complex hallucination text. LongHalQA is featured by GPT4V-generated hallucinatory data that are well aligned with real-world scenarios.
  arXiv  Detail & Related papers  (2024-10-13T18:59:58Z)
• Mitigating Entity-Level Hallucination in Large Language Models [11.872916697604278]
  This paper proposes Dynamic Retrieval Augmentation based on hallucination Detection (DRAD) as a novel method to detect and mitigate hallucinations in Large Language Models (LLMs) Experiment results show that DRAD demonstrates superior performance in both detecting and mitigating hallucinations in LLMs.
  arXiv  Detail & Related papers  (2024-07-12T16:47:34Z)
• Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models [70.19081534515371]
  Large Language Models (LLMs) have gained widespread adoption in various natural language processing tasks. They generate unfaithful or inconsistent content that deviates from the input source, leading to severe consequences. We propose a robust discriminator named RelD to effectively detect hallucination in LLMs' generated answers.
  arXiv  Detail & Related papers  (2024-07-04T18:47:42Z)
• CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification [73.66920648926161]
  We introduce the concept of code hallucinations and propose a classification method for code hallucination based on execution verification. We present a dynamic detection algorithm called CodeHalu designed to detect and quantify code hallucinations. We also introduce the CodeHaluEval benchmark, which includes 8,883 samples from 699 tasks, to systematically and quantitatively evaluate code hallucinations.
  arXiv  Detail & Related papers  (2024-04-30T23:56:38Z)
• Exploring and Evaluating Hallucinations in LLM-Powered Code Generation [14.438161741833687]
  Large Language Models (LLMs) produce outputs that deviate from users' intent, exhibit internal inconsistencies, or misalign with factual knowledge. Existing work mainly focuses on investing the hallucination in the domain of natural language generation. We conduct a thematic analysis of the LLM-generated code to summarize and categorize the hallucinations present in it. We propose HalluCode, a benchmark for evaluating the performance of code LLMs in recognizing hallucinations.
  arXiv  Detail & Related papers  (2024-04-01T07:31:45Z)
• Retrieve Only When It Needs: Adaptive Retrieval Augmentation for Hallucination Mitigation in Large Language Models [68.91592125175787]
  Hallucinations pose a significant challenge for the practical implementation of large language models (LLMs) We present Rowen, a novel approach that enhances LLMs with a selective retrieval augmentation process tailored to address hallucinations.
  arXiv  Detail & Related papers  (2024-02-16T11:55:40Z)
• Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models [116.01843550398183]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks. LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
  arXiv  Detail & Related papers  (2023-09-03T16:56:48Z)

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.