Researchers have developed IntentGrasp, a comprehensive benchmark for evaluating the intent understanding capabilities of Large Language Models (LLMs) (S1).

IntentGrasp is constructed from 49 open-licensed corpora across 12 domains (S1).

The benchmark includes a training set of 262,759 instances and two evaluation sets: the All Set with 12,909 test cases and the Gem Set with 470 cases (S1).

Evaluations on 20 LLMs across 7 families, including GPT-5.4, Gemini-3.1-Pro, and Claude-Opus-4.7, showed unsatisfactory performance (S1).

Scores were below 60% on the All Set and below 25% on the Gem Set (S1).

Notably, 17 out of 20 models performed worse than a random-guess baseline on the Gem Set, while human performance was estimated at ~81.1% (S1).

To improve intent understanding, the researchers propose Intentional Fine-Tuning (IFT), fine-tuning models on the IntentGrasp training set (S1).

IFT yielded significant gains of 30+ F1 points on the All Set and 20+ points on the Gem Set (S1).

Leave-one-domain-out experiments demonstrated the strong cross-domain generalizability of IFT (S1).

The IntentGrasp data is available on Hugging Face, and the code is on GitHub (S1).

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