IntentGrasp: A Comprehensive Benchmark for Intent Understanding
Summary
This paper introduces IntentGrasp, a comprehensive benchmark for evaluating large language models' intent understanding capabilities, revealing poor performance across 20 tested models. It proposes Intentional Fine-Tuning (IFT) as a solution, which significantly improves model performance and demonstrates strong cross-domain generalizability.
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Paper page - IntentGrasp: A Comprehensive Benchmark for Intent Understanding
Source: https://huggingface.co/papers/2605.06832
Abstract
IntentGrasp is a benchmark for evaluating large language models’ intent understanding capability, demonstrating poor performance across 20 models and showing significant improvements with intentional fine-tuning.
Accurately understanding the intent behind speech, conversation, and writing is crucial to the development of helpful Large Language Model (LLM) assistants. This paper introduces IntentGrasp, a comprehensivebenchmarkfor evaluating theintent understandingcapability of LLMs. Derived from 49 high-quality, open-licensed corpora spanning 12 diverse domains, IntentGrasp is constructed through source datasets curation, intent label contextualization, and task format unification. IntentGrasp contains a large-scale training set of 262,759 instances and two evaluation sets: an All Set of 12,909 test cases and a more balanced and challenging Gem Set of 470 cases. Extensive evaluations on 20 LLMs across 7 families (including frontier models such as GPT-5.4, Gemini-3.1-Pro, and Claude-Opus-4.7) demonstrate unsatisfactory performance, with scores below 60% on All Set and below 25% on Gem set. Notably, 17 out of 20 tested models perform worse than a random-guess baseline (15.2%) on Gem Set, while the estimated human performance is ~81.1%, showing substantial room for improvement. To enhance such ability, this paper proposes IntentionalFine-Tuning(IFT), which fine-tunes the models on the training set in IntentGrasp, yielding significant gains of 30+ F1 points on All Set and 20+ points on Gem Set. Tellingly, the leave-one-domain-out (Lodo) experiments further demonstrate the strongcross-domain generalizabilityof IFT, verifying that it is a promising approach to substantially enhancing theintent understandingof LLMs. Overall, bybenchmarking and boostingintent understandingability, this study sheds light on a promising path towards more intentional, capable, and safe AI assistants for human benefits and social good.
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