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A Survey on In-context Learning

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/2301.00234

Summary

Large language models can tackle a new task simply by reading a few worked examples placed in their prompt, without any change to their weights. This ability is called in-context learning (ICL). Its results vary strongly with the prompt template and with which examples are chosen and how they are ordered. Why it works at all is still poorly understood. Researchers from Peking University and partner institutions therefore compiled what they call the first comprehensive survey devoted to ICL. They formalize ICL as the model scoring candidate answers conditioned on a demonstration set plus an optional instruction. They also distinguish it from prompt learning and from classical few-shot learning, which updates parameters. The literature is arranged into a taxonomy covering training-side methods, inference-time prompt design, and analyses of influencing factors and mechanisms. The survey also covers applications and open problems. For projects on under-resourced languages, its discussion of scarce annotated demonstrations is especially relevant, as is its discussion of reusing high-resource data.

Key points

  • Training-side methods. These include pre-training approaches (PICL, MEND, ICLM) and "warmup" tuning before inference (MetaICL, Super-NaturalInstructions, symbol tuning). Another example is FLAN, which tuned the 137B LaMDA-PT on over 60 instruction-templated datasets. Later work scaled instruction tuning to more than 1000 tasks.
  • Prompt design. This covers demonstration selection, which can be unsupervised (KATE, SG-ICL, Votek) or supervised (EPR, UDR). It also covers reformatting (ICV, AutoICL), ordering (GlobalE&LocalE, ICCL), instruction generation (APE, Self-Instruct) and scoring functions (Calibrate, Channel Models, kNN-Prompting).
  • Mechanism research. Explanations include induction heads, Bayesian-inference views, and interpretations of ICL as implicit gradient descent.
  • Applications. The survey reviews low-cost data annotation and generation, retrieval-augmented and safety-steered generation, and knowledge updating. For knowledge updating, ICL reportedly outperforms gradient-based editing.
  • Open challenges.
    • Generalization to low-resource languages and tasks.
    • Efficiency and scalability.
    • Long-context ICL, where adding more demonstrations can hurt performance. LongICLBench exposes this weakness on extreme-label classification.

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

707 papers of our library cite it (of the 140673 whose bibliographies we read); 27 of them have a page here, 680 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2301.00234 · arXiv 2301.00234

@misc{dong2023survey,
  title = {{A Survey on In-context Learning}},
  author = {Dong, Qingxiu and Li, Lei and Dai, Damai and Zheng, Ce and Ma, Jingyuan and Li, Rui and Xia, Heming and Xu, Jingjing and Wu, Zhiyong and Liu, Tianyu and Chang, Baobao and Sun, Xu and Li, Lei and Sui, Zhifang},
  year = {2023},
  doi = {10.48550/arxiv.2301.00234},
  eprint = {2301.00234},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2301.00234}
}

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