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        <title>Induct-Learn: Short Phrase Prompting with Instruction Induction</title>
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        <namePart type="given">Po-Chun</namePart>
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    <abstract>Large Language Models (LLMs) have demonstrated capability in “instruction induction,” generating instructions from demonstrations (input-output pairs). However, existing methods often rely on large datasets or numerous examples, which is impractical and costly in real-world scenarios. In this work, we propose a low-cost, task-level framework called Induct-Learn. It induces pseudo instructions from a few demonstrations and a short phrase, adding a CoT process into existing demonstrations. When encountering new problems, the learned pseudo instructions and demonstrations with the pseudo CoT process can be combined into a prompt to guide the LLM’s problem-solving process. We validate our approach on the BBH-Induct and Evals-Induct datasets, and the results show that the Induct-Learn framework outperforms state-of-the-art methods. We also exhibit cross-model adaptability and achieve superior performance at a lower cost compared to existing methods.</abstract>
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%0 Conference Proceedings
%T Induct-Learn: Short Phrase Prompting with Instruction Induction
%A Chen, Po-Chun
%A Wei, Sheng-Lun
%A Huang, Hen-Hsen
%A Chen, Hsin-Hsi
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F chen-etal-2024-induct
%X Large Language Models (LLMs) have demonstrated capability in “instruction induction,” generating instructions from demonstrations (input-output pairs). However, existing methods often rely on large datasets or numerous examples, which is impractical and costly in real-world scenarios. In this work, we propose a low-cost, task-level framework called Induct-Learn. It induces pseudo instructions from a few demonstrations and a short phrase, adding a CoT process into existing demonstrations. When encountering new problems, the learned pseudo instructions and demonstrations with the pseudo CoT process can be combined into a prompt to guide the LLM’s problem-solving process. We validate our approach on the BBH-Induct and Evals-Induct datasets, and the results show that the Induct-Learn framework outperforms state-of-the-art methods. We also exhibit cross-model adaptability and achieve superior performance at a lower cost compared to existing methods.
%U https://aclanthology.org/2024.emnlp-main.297
%P 5204-5231

Markdown (Informal)

[Induct-Learn: Short Phrase Prompting with Instruction Induction](https://aclanthology.org/2024.emnlp-main.297) (Chen et al., EMNLP 2024)

• Induct-Learn: Short Phrase Prompting with Instruction Induction (Chen et al., EMNLP 2024)

ACL

• Po-Chun Chen, Sheng-Lun Wei, Hen-Hsen Huang, and Hsin-Hsi Chen. 2024. Induct-Learn: Short Phrase Prompting with Instruction Induction. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 5204–5231, Miami, Florida, USA. Association for Computational Linguistics.