Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions
<|reference_start|>Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions: We measure the performance of in-context learning as a function of task novelty and difficulty for open and closed questions. For that purpose, we created a novel benchmark consisting of hard scientific questions, each paired with a context of various relevancy. We show that counter-intuitively, a context that is more aligned with the topic does not always help more than a less relevant context. This effect is especially visible for open questions and questions of high difficulty or novelty. This result reveals a fundamental difference between the treatment of close-form and open-form questions by large-language models and shows a need for a more robust evaluation of in-context learning on the variety of different types of questions. It also poses a new question of how to optimally select a context for large language models, especially in the context of Retrieval Augmented Generation (RAG) systems. Our results suggest that the answer to this question can be highly application-dependent and might be contingent on factors including the format of the question, the perceived difficulty level of the questions, and the novelty or popularity of the information we seek.<|reference_end|>
@article{li2024why,
    title={Why does in-context learning fail sometimes? Evaluating in-context
  learning on open and closed questions},
    author={Xiang Li, Haoran Tang, Siyu Chen, Ziwei Wang, Ryan Chen, and Marcin
  Abram},
    journal={arXiv preprint arXiv:2407.02028},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02028},
    primaryClass={cs.CL cs.AI cs.IR cs.LG}
}