Exploring and Evaluating Hallucinations in LLM-Powered Code Generation

• URL: http://arxiv.org/abs/2404.00971v2
• Date: Sat, 11 May 2024 02:50:50 GMT
• Title: Exploring and Evaluating Hallucinations in LLM-Powered Code Generation
• Authors: Fang Liu, Yang Liu, Lin Shi, Houkun Huang, Ruifeng Wang, Zhen Yang, Li Zhang, Zhongqi Li, Yuchi Ma,
• Abstract summary: 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.
• Score: 14.438161741833687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rise of Large Language Models (LLMs) has significantly advanced many applications on software engineering tasks, particularly in code generation. Despite the promising performance, LLMs are prone to generate hallucinations, which means LLMs might produce outputs that deviate from users' intent, exhibit internal inconsistencies, or misalign with the factual knowledge, making the deployment of LLMs potentially risky in a wide range of applications. Existing work mainly focuses on investing the hallucination in the domain of natural language generation (NLG), leaving a gap in understanding the types and extent of hallucinations in the context of code generation. To bridge the gap, we conducted a thematic analysis of the LLM-generated code to summarize and categorize the hallucinations present in it. Our study established a comprehensive taxonomy of hallucinations in LLM-generated code, encompassing 5 primary categories of hallucinations depending on the conflicting objectives and varying degrees of deviation observed in code generation. Furthermore, we systematically analyzed the distribution of hallucinations, exploring variations among different LLMs and their correlation with code correctness. Based on the results, we proposed HalluCode, a benchmark for evaluating the performance of code LLMs in recognizing hallucinations. Hallucination recognition and mitigation experiments with HalluCode and HumanEval show existing LLMs face great challenges in recognizing hallucinations, particularly in identifying their types, and are hardly able to mitigate hallucinations. We believe our findings will shed light on future research about hallucination evaluation, detection, and mitigation, ultimately paving the way for building more effective and reliable code LLMs in the future.

Related papers

• A Survey of Hallucination in Large Visual Language Models [48.794850395309076]
  The existence of hallucinations has limited the potential and practical effectiveness of LVLM in various fields. The structure of LVLMs and main causes of hallucination generation are introduced. The available hallucination evaluation benchmarks for LVLMs are presented.
  arXiv  Detail & Related papers  (2024-10-20T10:58:58Z)
• 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)
• LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation [33.46342144822026]
  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.
  arXiv  Detail & Related papers  (2024-09-30T17:51:15Z)
• 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)
• The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models [134.6697160940223]
  hallucination poses great challenge to trustworthy and reliable deployment of large language models. Three key questions should be well studied: how to detect hallucinations (detection), why do LLMs hallucinate (source), and what can be done to mitigate them. This work presents a systematic empirical study on LLM hallucination, focused on the the three aspects of hallucination detection, source and mitigation.
  arXiv  Detail & Related papers  (2024-01-06T12:40:45Z)
• A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions [40.79317187623401]
  The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP) LLMs are prone to hallucination, generating plausible yet nonfactual content. This phenomenon raises significant concerns over the reliability of LLMs in real-world information retrieval systems.
  arXiv  Detail & Related papers  (2023-11-09T09:25:37Z)
• 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)
• Evaluating Object Hallucination in Large Vision-Language Models [122.40337582958453]
  This work presents the first systematic study on object hallucination of large vision-language models (LVLMs) We find that LVLMs tend to generate objects that are inconsistent with the target images in the descriptions. We propose a polling-based query method called POPE to evaluate the object hallucination.
  arXiv  Detail & Related papers  (2023-05-17T16:34:01Z)

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.