Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

• URL: http://arxiv.org/abs/2309.01219v2
• Date: Sun, 24 Sep 2023 16:03:24 GMT
• Title: Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
• Authors: Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, Shuming Shi
• Abstract summary: 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.
• Score: 116.01843550398183
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge. This phenomenon poses a substantial challenge to the reliability of LLMs in real-world scenarios. In this paper, we survey recent efforts on the detection, explanation, and mitigation of hallucination, with an emphasis on the unique challenges posed by LLMs. We present taxonomies of the LLM hallucination phenomena and evaluation benchmarks, analyze existing approaches aiming at mitigating LLM hallucination, and discuss potential directions for future research.

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)
• SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection [10.54378596443678]
  Large language models (LLMs) are highly capable but face latency challenges in real-time applications. This study optimize the real-time interpretable hallucination detection by introducing effective prompting techniques.
  arXiv  Detail & Related papers  (2024-08-22T22:13:13Z)
• 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)
• Hallucination of Multimodal Large Language Models: A Survey [40.73148186369018]
  multimodal large language models (MLLMs) have demonstrated significant advancements and remarkable abilities in multimodal tasks. Despite these promising developments, MLLMs often generate outputs that are inconsistent with the visual content. This survey aims to deepen the understanding of hallucinations in MLLMs and inspire further advancements in the field.
  arXiv  Detail & Related papers  (2024-04-29T17:59:41Z)
• 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)
• 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)
• Towards Mitigating Hallucination in Large Language Models via Self-Reflection [63.2543947174318]
  Large language models (LLMs) have shown promise for generative and knowledge-intensive tasks including question-answering (QA) tasks. This paper analyses the phenomenon of hallucination in medical generative QA systems using widely adopted LLMs and datasets.
  arXiv  Detail & Related papers  (2023-10-10T03:05:44Z)

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.