2025
EMNLP
EMNLP 2025
DispatchQA: A Benchmark for Small Function Calling Language Models in E-Commerce Applications
Abstract
AbstractWe 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.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— api calling
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Application Areas > Efficient Computing
Natural Language Processing > Applications > Information Retrieval
Natural Language Processing > Applications > Question Answering
Natural Language Processing > Resources & Methods > Large Language Models
Artificial Intelligence > Core AI > Large Language Models
Deep Learning > Models > Large Language Models
Machine Learning > Learning Types > Fine-Tuning
Machine Learning > Application Areas > Recommender Systems
Machine Learning > Application Areas > Information Retrieval
Deep Learning > Learning Types > Fine-Tuning