[2605.06832] IntentGrasp: A Comprehensive Benchmark for Intent Understanding
[Submitted on 7 May 2026]
Title:IntentGrasp: A Comprehensive Benchmark for Intent Understanding
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Abstract: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 comprehensive benchmark for evaluating the intent understanding capability 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 Intentional Fine-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 strong cross-domain generalizability of IFT, verifying that it is a promising approach to substantially enhancing the intent understanding of LLMs. Overall, by benchmarking and boosting intent understanding ability, this study sheds light on a promising path towards more intentional, capable, and safe AI assistants for human benefits and social good.
Comments: IntentGrasp data is available on [Hugging Face](this https URL), and the code is released on [GitHub](this https URL)
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
ACM classes: I.2.7
Cite as: arXiv:2605.06832 [cs.CL]
(or arXiv:2605.06832v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.06832
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yuwei Yin [view email] [v1] Thu, 7 May 2026 18:34:27 UTC (439 KB)
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