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home / papers / 2023 / A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive licence to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2311.05232

Summary

Large language models such as GPT-4, LLaMA, Claude and Gemini often produce fluent but unsupported content. This "hallucination" undermines their use in chatbots, search engines and recommender systems. Older work on hallucination targeted task-specific NLG models and split errors into intrinsic and extrinsic types. The authors argue that this split does not suit open-ended, general-purpose LLMs, so they propose a new taxonomy built on two axes: factuality and faithfulness. The survey then traces where hallucinations originate across the data, training and inference stages. It reviews detection methods and evaluation benchmarks, and it organizes mitigation techniques so that each one matches the cause it addresses. A separate part examines why retrieval-augmented generation (RAG) still hallucinates and what that means for building sturdier IR systems. The survey closes with open problems, chiefly hallucination in vision-language models and how well LLMs know the limits of their own knowledge. The paper is useful as a structured map of the field and as a guide for choosing remedies based on root causes.

Key points

  • Taxonomy. The survey distinguishes two main types:
    • Factuality hallucination: output conflicts with verifiable real-world facts.
    • Faithfulness hallucination: output diverges from the input or from itself. It has three subtypes: instruction inconsistency, context inconsistency and logical inconsistency.
  • Causes by stage. Each stage has its own sources:
    • Data: misinformation and biases, knowledge-boundary gaps, low-quality alignment data.
    • Training: pre-training, SFT and RLHF (e.g., sycophancy).
    • Inference: imperfect decoding, over-confidence, the softmax bottleneck and reasoning failures.
  • Mitigation. Strategies are grouped into data-, training- and inference-related approaches, each linked to the cause it targets. RAG gets its own analysis of its limitations.
  • LVLMs. Vision-language models invent objects, attributes and relations, and they lean too heavily on language priors. Current fixes typically depend on extra annotation, expert models or additional training.
  • Publication. The paper appeared in ACM Transactions on Information Systems (58 pages, DOI 10.1145/3703155). The arXiv ID is 2311.05232.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

842 papers of our library cite it (of the 140673 whose bibliographies we read); 10 of them have a page here, 832 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1145/3703155 · arXiv 2311.05232

@misc{huang2023survey,
  title = {{A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions}},
  author = {Huang, Lei and Yu, Weijiang and Ma, Weitao and Zhong, Weihong and Feng, Zhangyin and Wang, Haotian and Chen, Qianglong and Peng, Weihua and Feng, Xiaocheng and Qin, Bing and Liu, Ting},
  year = {2023},
  doi = {10.1145/3703155},
  eprint = {2311.05232},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1145/3703155}
}

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