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        <title>Task-Level Instructions Induction for Audio Question Answering from Few Examples</title>
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    <name type="personal">
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    <abstract>Large audio-language models (LALMs) benefit from Chain-of-Thought (CoT) prompting for audio question answering (AQA), but acquiring audio CoT examples is particularly challenging as it requires sequential listening and careful integration of acoustic and linguistic information. Surprisingly, our experiments reveal that standard few-shot prompting yields inconsistent results compared to zero-shot CoT, with several models showing degraded accuracy. Moreover, few-shot prompting incurs substantially higher inference costs by processing multiple audio demonstrations per inference. We propose Audio-Induct, which induces reusable textual task instructions from few audio examples once per task, requiring no additional demonstrations at inference. Evaluated on 9 LALMs across two benchmarks, Audio-Induct outperforms state-of-the-art prompting methods while maintaining low inference costs. Inducted Task Instructions transfer effectively across models, enabling scalable deployment.</abstract>
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%0 Conference Proceedings
%T Task-Level Instructions Induction for Audio Question Answering from Few Examples
%A Chen, Po-Chun
%A Huang, Hen-Hsen
%A Chen, Hsin-Hsi
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-381-4
%F chen-etal-2026-task
%X Large audio-language models (LALMs) benefit from Chain-of-Thought (CoT) prompting for audio question answering (AQA), but acquiring audio CoT examples is particularly challenging as it requires sequential listening and careful integration of acoustic and linguistic information. Surprisingly, our experiments reveal that standard few-shot prompting yields inconsistent results compared to zero-shot CoT, with several models showing degraded accuracy. Moreover, few-shot prompting incurs substantially higher inference costs by processing multiple audio demonstrations per inference. We propose Audio-Induct, which induces reusable textual task instructions from few audio examples once per task, requiring no additional demonstrations at inference. Evaluated on 9 LALMs across two benchmarks, Audio-Induct outperforms state-of-the-art prompting methods while maintaining low inference costs. Inducted Task Instructions transfer effectively across models, enabling scalable deployment.
%U https://aclanthology.org/2026.eacl-short.18/
%P 244-264

Markdown (Informal)

[Task-Level Instructions Induction for Audio Question Answering from Few Examples](https://aclanthology.org/2026.eacl-short.18/) (Chen et al., EACL 2026)

• Task-Level Instructions Induction for Audio Question Answering from Few Examples (Chen et al., EACL 2026)

ACL

• Po-Chun Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2026. Task-Level Instructions Induction for Audio Question Answering from Few Examples. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 244–264, Rabat, Morocco. Association for Computational Linguistics.