@inproceedings{daiber-etal-2025-dispatchqa,
    title = "{D}ispatch{QA}: A Benchmark for Small Function Calling Language Models in {E}-Commerce Applications",
    author = "Daiber, Joachim  and
      Maricato, Victor  and
      Sinha, Ayan  and
      Rabinovich, Andrew",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.154/",
    pages = "2221--2233",
    ISBN = "979-8-89176-333-3",
    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{\texttimes} faster inference. All data, code, and training checkpoints are publicly released to spur further research on resource{-}efficient query understanding."
}