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        <title>Learning to Retrieve In-Context Examples for Large Language Models</title>
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        <namePart type="given">Liang</namePart>
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    <name type="personal">
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            <title>Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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    <abstract>Large language models (LLMs) have demonstrated their ability to learn in-context, allowing them to perform various tasks based on a few input-output examples. However, the effectiveness of in-context learning is heavily reliant on the quality of the selected examples. In this paper, we propose a novel framework to iteratively train dense retrievers that can identify high-quality in-context examples for LLMs. Our framework initially trains a reward model based on LLM feedback to evaluate the quality of candidate examples, followed by knowledge distillation to train a bi-encoder based dense retriever. Our experiments on a suite of 30 tasks demonstrate that our framework significantly enhances in-context learning performance. Furthermore, we show the generalization ability of our framework to unseen tasks during training. An in-depth analysis reveals that our model improves performance by retrieving examples with similar patterns, and the gains are consistent across LLMs of varying sizes.</abstract>
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        <url>https://aclanthology.org/2024.eacl-long.105</url>
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%0 Conference Proceedings
%T Learning to Retrieve In-Context Examples for Large Language Models
%A Wang, Liang
%A Yang, Nan
%A Wei, Furu
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F wang-etal-2024-learning
%X Large language models (LLMs) have demonstrated their ability to learn in-context, allowing them to perform various tasks based on a few input-output examples. However, the effectiveness of in-context learning is heavily reliant on the quality of the selected examples. In this paper, we propose a novel framework to iteratively train dense retrievers that can identify high-quality in-context examples for LLMs. Our framework initially trains a reward model based on LLM feedback to evaluate the quality of candidate examples, followed by knowledge distillation to train a bi-encoder based dense retriever. Our experiments on a suite of 30 tasks demonstrate that our framework significantly enhances in-context learning performance. Furthermore, we show the generalization ability of our framework to unseen tasks during training. An in-depth analysis reveals that our model improves performance by retrieving examples with similar patterns, and the gains are consistent across LLMs of varying sizes.
%U https://aclanthology.org/2024.eacl-long.105
%P 1752-1767

Markdown (Informal)

[Learning to Retrieve In-Context Examples for Large Language Models](https://aclanthology.org/2024.eacl-long.105) (Wang et al., EACL 2024)

• Learning to Retrieve In-Context Examples for Large Language Models (Wang et al., EACL 2024)

ACL

• Liang Wang, Nan Yang, and Furu Wei. 2024. Learning to Retrieve In-Context Examples for Large Language Models. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1752–1767, St. Julian’s, Malta. Association for Computational Linguistics.