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        <title>DispatchQA: A Benchmark for Small Function Calling Language Models in E-Commerce Applications</title>
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    <name type="personal">
        <namePart type="given">Joachim</namePart>
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        <namePart type="given">Victor</namePart>
        <namePart type="family">Maricato</namePart>
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    <name type="personal">
        <namePart type="given">Ayan</namePart>
        <namePart type="family">Sinha</namePart>
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    <name type="personal">
        <namePart type="given">Andrew</namePart>
        <namePart type="family">Rabinovich</namePart>
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        <dateIssued>2025-11</dateIssued>
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            <title>Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track</title>
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            <namePart type="given">Saloni</namePart>
            <namePart type="family">Potdar</namePart>
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        <name type="personal">
            <namePart type="given">Lina</namePart>
            <namePart type="family">Rojas-Barahona</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Sebastien</namePart>
            <namePart type="family">Montella</namePart>
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    <abstract>We introduce DispatchQA, a benchmark to evaluate how well small language models (SLMs) translate open-ended search queries into executable API calls via explicit function calling. Our benchmark focuses on the latency-sensitive e-commerce setting and measures SLMs’ impact on both search relevance and search latency. We provide strong, replicable baselines based on Llama 3.1 8B Instruct fine-tuned on synthetically generated data and find that fine-tuned SLMs produce search quality comparable or better than large language models such as GPT-4o while achieving up to 3× faster inference. All data, code, and training checkpoints are publicly released to spur further research on resource-efficient query understanding.</abstract>
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        <date>2025-11</date>
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            <start>2221</start>
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%0 Conference Proceedings
%T DispatchQA: A Benchmark for Small Function Calling Language Models in E-Commerce Applications
%A Daiber, Joachim
%A Maricato, Victor
%A Sinha, Ayan
%A Rabinovich, Andrew
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F daiber-etal-2025-dispatchqa
%X We introduce DispatchQA, a benchmark to evaluate how well small language models (SLMs) translate open-ended search queries into executable API calls via explicit function calling. Our benchmark focuses on the latency-sensitive e-commerce setting and measures SLMs’ impact on both search relevance and search latency. We provide strong, replicable baselines based on Llama 3.1 8B Instruct fine-tuned on synthetically generated data and find that fine-tuned SLMs produce search quality comparable or better than large language models such as GPT-4o while achieving up to 3× faster inference. All data, code, and training checkpoints are publicly released to spur further research on resource-efficient query understanding.
%U https://aclanthology.org/2025.emnlp-industry.154/
%P 2221-2233

Markdown (Informal)

[DispatchQA: A Benchmark for Small Function Calling Language Models in E-Commerce Applications](https://aclanthology.org/2025.emnlp-industry.154/) (Daiber et al., EMNLP 2025)

• DispatchQA: A Benchmark for Small Function Calling Language Models in E-Commerce Applications (Daiber et al., EMNLP 2025)

ACL

• Joachim Daiber, Victor Maricato, Ayan Sinha, and Andrew Rabinovich. 2025. DispatchQA: A Benchmark for Small Function Calling Language Models in E-Commerce Applications. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2221–2233, Suzhou (China). Association for Computational Linguistics.