# IntentGrasp: A Comprehensive Benchmark for Intent Understanding

Source: [IntentGrasp: A Comprehensive Benchmark for Intent Understanding](<https://www.sota2.com/research/paper/6a01628adbc836b87d823071>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-14T06:00:04.370Z

Authors: Yuwei Yin, Chuyuan Li, Giuseppe Carenini

Published: 2026-05-07T18:34:27.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Intent Grasping on IntentGrasp Gem Set](<https://www.sota2.com/research/sota/intent-grasping-on-intentgrasp-gem-set.md>): Intent Grasping; IntentGrasp Gem Set; F1 Score: not recorded.

- [Intent Grasping on IntentGrasp All Set (downsample)](<https://www.sota2.com/research/sota/intent-grasping-on-intentgrasp-all-set-downsample.md>): Intent Grasping; IntentGrasp All Set (downsample); F1 Score: not recorded.

- [Intent Classification on IntentGrasp All Set](<https://www.sota2.com/research/sota/intent-classification-on-intentgrasp-all-set.md>): Intent Classification; IntentGrasp All Set; Accuracy (No Shuffle): not recorded.

- [Intent Classification on IntentGrasp Gem Set](<https://www.sota2.com/research/sota/intent-classification-on-intentgrasp-gem-set.md>): Intent Classification; IntentGrasp Gem Set; Performance (No Shuffle): not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2605.06832>)

[PDF](<https://arxiv.org/pdf/2605.06832.pdf>)

[Implementation](<https://github.com/YuweiYin/IntentGrasp>)