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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — api calling
🐝 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