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Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation

Paper · 2025 · Ruiyang Ren, Yuhao Wang, Yingqi Qu et al.

Summary

This COLING 2025 study asks whether large language models know where their factual knowledge ends, and how retrieval augmentation changes that self-awareness in open-domain QA. The authors test closed models (Davinci003, ChatGPT, GPT-4) and open models (LLaMA2, Mistral) on benchmarks including NQ and TriviaQA. Each model answers questions with and without supporting documents. It also makes two kinds of self-assessment: a priori judgement (deciding beforehand whether it can answer) and a posteriori judgement (checking its own answer afterwards). Documents come from a dense retriever (RocketQAv2 with Faiss), a sparse one (BM25), and ChatGPT-generated passages. Document quality is also varied deliberately, from golden passages to random incorrect ones.

The models turn out to be overconfident and poorly calibrated about their own knowledge. Retrieval improves both accuracy and boundary awareness, particularly for smaller models. However, models lean heavily on whatever documents they receive, even misleading ones. The authors also propose a simple scheme that uses priori judgement to decide when to retrieve. The results matter for building reliable retrieval-augmented systems.

Key points

  • Evaluation uses EM and F1 for answers, plus new judgement metrics: Give-up, Right/G, Right/¬G, Eval-Right and Eval-Acc.
  • Overconfidence example: on NQ without retrieval, GPT-4 judges 90.20% of its answers correct (Eval-Right), yet its judgement accuracy (Eval-Acc) is only 38.87%. Its EM is 34.60.
  • With dense-retrieved documents on NQ, GPT-4's EM rises from 34.60 to 43.60, and with golden documents to 53.60. LLaMA2 gains more, going from 16.60 to 33.40 (retrieved) and 48.60 (golden).
  • Incorrect but relevant documents hurt performance below the no-retrieval baseline. For example, GPT-4 reaches only 21.60 EM with high-related wrong passages. Random incorrect documents do less damage, and LLaMA2 gives up on 93.40% of questions when given them.
  • Code is released at github.com/RUCAIBox/LLM-Knowledge-Boundary.

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

 Institutions

🏢BaiduBeijing · China40 works · 14,627 citations🎓Renmin University of ChinaBeijing · China35 works · 14,757 citations

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 worksComputer Vision and Pattern Recognition · 1,282 worksInformation Systems · 523 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1,343 worksInformation retrieval & search · 145 works

Cite

Cite as Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hua Wu, Ji-Rong Wen and Haifeng Wang. 2025. Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation. Proceedings of the 31st International Conference on Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{ren2025investigating,
  title = {{Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation}},
  author = {Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hua Wu, Ji-Rong Wen and Haifeng Wang},
  booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
  year = {2025},
  url = {https://aclanthology.org/2025.coling-main.250/}
}

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