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Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity

Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2310.07521

Summary

Large language models are increasingly used as knowledge sources in search, chat assistants and high-stakes fields such as medicine, law and finance, yet they often state things that contradict established facts. This survey treats the "factuality issue", meaning how likely an LLM is to produce fact-inconsistent output, as separate from the broader hallucination problem. It organizes the literature around four questions: what the issue is and why it matters, how it is measured, why it arises, and how it can be reduced. Two deployment settings are examined separately: standalone models such as ChatGPT and retrieval-augmented systems such as BingChat. The authors catalogue evaluation metrics and benchmarks and trace errors to the model, retrieval and inference stages. They then review mitigation methods, including pretraining, fine-tuning, model editing, multi-agent debate, decoding adjustments and several retrieval designs, along with many domain-tuned LLMs. The outcome is a structured taxonomy and reading guide, supported by a maintained GitHub repository (wangcunxiang/LLM-Factuality-Survey). It is useful for building trustworthy domain-specific NLP systems.

Key points

  • Evaluation is grouped into rule-based, neural, human and LLM-based metrics. Benchmarks covered include MMLU, TruthfulQA, C-Eval, AGIEval, RealTimeQA and FreshQA, and domain benchmarks include FLARE (finance), CMB (medicine), GeneTuring and EcomInstruct.
  • Error causes are split into three levels:
    • Model-level: domain knowledge gaps, outdated information, forgetting and reasoning failures.
    • Retrieval-level: insufficient or distracting context, and misinformation the model fails to detect.
    • Inference-level: snowballing, erroneous decoding and exposure bias.
  • Retrieval-augmented enhancement covers interactive retrieval (IRCoT, ReAct), retrieval adaptation (ATLAS, REPLUG, SAIL, GopherCite), external memory, and structured-data retrieval (StructGPT).
  • BloombergGPT is a 50B-parameter BLOOM-style model trained on 363B tokens, of which 51.27% is Bloomberg financial data.
  • Geoscience resources are relevant to geography projects:
    • K2: LLaMA-7B further trained on a 5.5B-token geoscience corpus, released with the GeoSignal instruction set and the GeoBench benchmark.
    • HouYi: a renewable-energy model built on the REAP dataset of over 1.1M papers.
    • OceanGPT: an ocean-science model evaluated on OceanBench.

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: Natural language processing · AI & machine learning

Cited by

115 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 109 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2310.07521 · arXiv 2310.07521

@misc{wang2023survey,
  title = {{Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity}},
  author = {Wang, Cunxiang and Liu, Xiaoze and Yue, Yuanhao and Tang, Xiangru and Zhang, Tianhang and Jiayang, Cheng and Yao, Yunzhi and Gao, Wenyang and Hu, Xuming and Qi, Zehan and Wang, Yidong and Yang, Linyi and Wang, Jindong and Xie, Xing and Zhang, Zheng and Zhang, Yue},
  year = {2023},
  doi = {10.48550/arxiv.2310.07521},
  eprint = {2310.07521},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2310.07521}
}

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