IntentGrasp: A Comprehensive Benchmark for Intent Understanding
Researchers introduce IntentGrasp, a comprehensive benchmark dataset for evaluating how well large language models understand user intent across 12 diverse domains. Testing 20 frontier LLMs reveals widespread performance gaps, with most models scoring below 60% accuracy and many performing worse than random chance on challenging subsets, while a proposed fine-tuning method achieves 20-30+ point improvements.