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        <title>Disentangling Questions from Query Generation for Task-Adaptive Retrieval</title>
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    <name type="personal">
        <namePart type="given">Seung-won</namePart>
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            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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    <abstract>This paper studies the problem of information retrieval, to adapt to unseen tasks. Existing work generates synthetic queries from domain-specific documents to jointly train the retriever. However, the conventional query generator assumes the query as a question, thus failing to accommodate general search intents. A more lenient approach incorporates task-adaptive elements, such as few-shot learning with an 137B LLM. In this paper, we challenge a trend equating query and question, and instead conceptualize query generation task as a “compilation” of high-level intent into task-adaptive query. Specifically, we propose EGG, a query generator that better adapts to wide search intents expressed in the BeIR benchmark. Our method outperforms baselines and existing models on four tasks with underexplored intents, while utilizing a query generator 47 times smaller than the previous state-of-the-art. Our findings reveal that instructing the LM with explicit search intent is a key aspect of modeling an effective query generator.</abstract>
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%0 Conference Proceedings
%T Disentangling Questions from Query Generation for Task-Adaptive Retrieval
%A Lee, Yoonsang
%A Kim, Minsoo
%A Hwang, Seung-won
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F lee-etal-2024-disentangling
%X This paper studies the problem of information retrieval, to adapt to unseen tasks. Existing work generates synthetic queries from domain-specific documents to jointly train the retriever. However, the conventional query generator assumes the query as a question, thus failing to accommodate general search intents. A more lenient approach incorporates task-adaptive elements, such as few-shot learning with an 137B LLM. In this paper, we challenge a trend equating query and question, and instead conceptualize query generation task as a “compilation” of high-level intent into task-adaptive query. Specifically, we propose EGG, a query generator that better adapts to wide search intents expressed in the BeIR benchmark. Our method outperforms baselines and existing models on four tasks with underexplored intents, while utilizing a query generator 47 times smaller than the previous state-of-the-art. Our findings reveal that instructing the LM with explicit search intent is a key aspect of modeling an effective query generator.
%U https://aclanthology.org/2024.findings-emnlp.274
%P 4775-4785

Markdown (Informal)

[Disentangling Questions from Query Generation for Task-Adaptive Retrieval](https://aclanthology.org/2024.findings-emnlp.274) (Lee et al., Findings 2024)

• Disentangling Questions from Query Generation for Task-Adaptive Retrieval (Lee et al., Findings 2024)

ACL

• Yoonsang Lee, Minsoo Kim, and Seung-won Hwang. 2024. Disentangling Questions from Query Generation for Task-Adaptive Retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 4775–4785, Miami, Florida, USA. Association for Computational Linguistics.