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Self-Knowledge Guided Retrieval Augmentation for Large Language Models

Paper · 2023 · Yile Wang, Peng Li, Maosong Sun et al.

Summary

Retrieval augmentation is often treated as universally helpful for large language models, but the authors show that retrieved passages can sometimes distract a model from answers it would otherwise get right. They propose SKR (Self-Knowledge guided Retrieval augmentation), which decides per question whether to use retrieval. First, each training question is answered by the LLM with few-shot prompting, both with and without passages fetched by a dense passage retriever from Wikipedia. Each question is then labeled as an "LLM known" if retrieval does not improve the result, or an "LLM unknown" if it does. To classify a new question, the authors test four detectors: direct prompting, in-context learning with labeled examples, a small trained classifier, and k-nearest-neighbor search over similar training questions. Retrieval is triggered only for questions predicted to be unknown. Across five QA datasets, the kNN variant works best and beats both chain-of-thought and always-retrieve baselines. The result suggests that letting a model judge its own knowledge boundaries makes the use of external knowledge more reliable.

Key points

  • Models evaluated: InstructGPT (text-davinci-003) and ChatGPT (gpt-3.5-turbo-0301), accessed through the OpenAI API. Code is at github.com/THUNLP-MT/SKR.
  • SKR outperforms chain-of-thought and fully retrieval-based methods by 4.08% and 2.91% with InstructGPT, and by 4.02% and 4.20% with ChatGPT.
  • The nearest-neighbor detector is the most effective of the four strategies. It is also cheaper than the prompting-based ones, which require an LLM call for every new question.
  • The five evaluation datasets are TemporalQA, CommonsenseQA, TabularQA, StrategyQA and TruthfulQA.
  • Wikipedia gives the best average result as a retrieval corpus, with a large advantage on entity-centric TabularQA. MS MARCO and SciFact perform comparably on CommonsenseQA and TruthfulQA.

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

🎓Tsinghua UniversityBeijing · China137 works · 83,262 citations🔬Shanghai Artificial Intelligence LaboratoryShanghai · China44 works · 10,162 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 Yile Wang, Peng Li, Maosong Sun and Yang Liu. 2023. Self-Knowledge Guided Retrieval Augmentation for Large Language Models. Findings of the Association for Computational Linguistics: EMNLP 2023.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.findings-emnlp.691

@inproceedings{wang2023self,
  title = {{Self-Knowledge Guided Retrieval Augmentation for Large Language Models}},
  author = {Yile Wang, Peng Li, Maosong Sun and Yang Liu},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023},
  year = {2023},
  doi = {10.18653/v1/2023.findings-emnlp.691},
  url = {https://doi.org/10.18653/v1/2023.findings-emnlp.691}
}

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