IntentGrasp: A Comprehensive Benchmark for Intent Understanding
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| Authors | Yuwei Yin et al. |
| Year | 2026 |
| HF Upvotes | 2 |
| arXiv | 2605.06832 |
| Download | |
| Code | https://github.com/YuweiYin/IntentGrasp |
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.
Engineering Breakdown
Plain English
IntentGrasp is a benchmark dataset for evaluating how well LLMs understand user intent across speech, conversation, and writing tasks. Built from 49 existing datasets across 12 domains, it provides 262,759 training instances and two test sets (12,909 general cases plus 470 harder balanced cases), with evaluations showing how 20 LLMs from 7 families perform on intent classification—essentially measuring whether models actually understand what users are asking for, not just pattern matching.
Key Engineering Insight
The paper identifies that intent understanding is separable from general LLM capability and measurable through a unified benchmark, meaning you can now directly test whether your model's ability to classify user goals improves or degrades with scale, architecture, or fine-tuning—this gives you a diagnostic tool for a specific failure mode rather than relying on general benchmarks.
Why It Matters for Engineers
Production LLM assistants fail when they misclassify user intent (answering the wrong question confidently), which costs you in support tickets, user churn, and safety issues. Having a standardized benchmark means you can measure intent understanding as a distinct capability during model selection, before deployment, and catch regressions—this is the difference between 'our model seems to work' and 'we know our model classifies intents correctly 94% of the time across our actual use cases.'
Research Context
Previous work evaluated intent understanding using scattered, domain-specific datasets without standardization, making it impossible to compare models fairly or identify systematic gaps. IntentGrasp consolidates this by unifying 49 datasets under one evaluation framework, enabling consistent measurement across LLM families and enabling future research to build on a shared baseline rather than fragmenting effort across incompatible benchmarks.
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