Finding Support Examples for In-Context Learning

• URL: http://arxiv.org/abs/2302.13539v3
• Date: Mon, 9 Oct 2023 02:39:04 GMT
• Title: Finding Support Examples for In-Context Learning
• Authors: Xiaonan Li, Xipeng Qiu
• Abstract summary: We propose LENS, a fiLter-thEN-Search method to tackle this challenge in two stages. First we filter the dataset to obtain informative in-context examples individually. Then we propose diversity-guided example search which iteratively refines and evaluates the selected example permutations.
• Score: 73.90376920653507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Additionally, the strong dependency among in-context examples makes it an NP-hard combinatorial optimization problem and enumerating all permutations is infeasible. Hence we propose LENS, a fiLter-thEN-Search method to tackle this challenge in two stages: First we filter the dataset to obtain informative in-context examples individually. Specifically, we propose a novel metric, InfoScore, to evaluate the example's in-context informativeness based on the language model's feedback, and further propose a progressive filtering process to filter out uninformative examples. Then we propose diversity-guided example search which iteratively refines and evaluates the selected example permutations, to find examples that fully depict the task. The experimental results show that LENS significantly outperforms a wide range of baselines.

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