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IntentGrasp: A Comprehensive Benchmark for Intent Understanding

Yuwei Yin, Chuyuan Li, Giuseppe Carenini

arXiv:2605.06832v1cs.CLcs.AIcs.LG

TL;DR

LLMs still struggle to understand intent across diverse communication settings, limiting evidence for reliable intent understanding. IntentGrasp benchmarks this ability across 12 domains and evaluates Intentional Fine-Tuning, which substantially improves performance and generalizes across domains.

  • Problem

    LLMs’ intent understanding across speech, conversation, and writing remains insufficient despite its importance for communication, problem-solving, and safe assistance.

  • Method

    IntentGrasp standardizes 49 datasets across 12 domains into a unified benchmark, and Intentional Fine-Tuning trains models on its training set.

  • Results

    All tested models score below 60% on All Set and below 25% on Gem Set, while Intentional Fine-Tuning gains 30+ F1 points on All Set and 20+ on Gem Set.

  • Takeaways & Limitations

    IntentGrasp identifies substantial room to improve LLM intent understanding, while Intentional Fine-Tuning offers a promising cross-domain direction for more capable assistants.

  • Takeaways & Limitations

    Even after Intentional Fine-Tuning, models still struggle in news and policy-making domains, with F1 scores below 15%.

Abstract

from arXiv · show

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.

1 Introduction

IntentGrasp addresses fragmented, heterogeneous intent-classification resources by standardizing 49 open-licensed datasets across 12 domains and evaluating intent understanding in 20 LLMs. The benchmark reveals substantial weaknesses even in frontier models, while Intentional Fine-Tuning produces large, cross-domain improvements.

  • Motivation: Intent understanding is crucial for communication, problem-solving, and safe AI assistance because misunderstood or malicious intent can produce harmful advice, instructions, or content.Examples include incorrect medication doses, misinterpreted contract clauses, unreliable financial advice, and bypassed safeguards.
  • Problem: Existing intent-classification datasets are fragmented and heterogeneous, often concentrating on limited domains such as flight booking and banking inquiries.These limitations motivate a standardized benchmark for evaluating LLM intent understanding.
  • Benchmark: IntentGrasp curates 49 high-quality open-licensed datasets across 12 diverse domains and covers queries, dialogues, and monologues.Its construction also includes contextualizing ambiguous intent labels into enriched clause-like intent statements.
  • Evaluation: 20 frontier LLMs across 7 families, including GPT-5.4, Gemini-3.1-Pro, and Claude-Opus-4.7, all score below 60% F1.The evaluation identifies considerable room for improvement in intent understanding, including among state-of-the-art models.
  • Intentional Fine-Tuning: 30+ F1 points on All Set and 20+ points on Gem Set are gained by Intentional Fine-Tuning over baseline methods.The improvement is consistent across all 12 domains, with particularly significant boosts in daily life, e-commerce, and empathetic response; leave-one-domain-out experiments further assess cross-domain generalizability.

2 Related Work

Intent classification has a long history and many datasets, but their heterogeneous structures and ambiguous labels complicate comprehensive LLM evaluation. Existing LLM benchmarks have only recently begun targeting intent understanding, while broader benchmarks emphasize multidisciplinary problem-solving and general reasoning.

  • Intent Classification Datasets: Intent classification is a longstanding NLP task supported by numerous datasets, but heterogeneous data structures and ambiguous intent labels hinder direct, comprehensive LLM assessment.
  • LLM Benchmarks for Intent Understanding: Intent understanding is central to contextual and pragmatic language understanding, yet most comprehensive LLM benchmarks prioritize multidisciplinary problem-solving and general reasoning.
  • LLM Benchmarks for Intent Understanding: IntentGrasp addresses these challenges by curating 49 high-quality open-licensed datasets across 12 domains, contextualizing ambiguous labels, and unifying instances as question-answering tasks.

3 IntentGrasp Benchmark

IntentGrasp is constructed by curating 49 high-quality, open-licensed datasets across 12 domains, contextualizing inconsistent intent labels, and unifying heterogeneous instances into a multiple-choice QA benchmark. Its resulting splits balance source-dataset representation while retaining diverse intent-understanding scenarios.

  • Stage 1: Source Datasets Curation: 49 source datasets spanning 12 diverse domains are collected and parsed to comprehensively evaluate intent understanding.The datasets are selected for relevance, quality, and open licensing.
  • Stage 2: Intent Label Contextualization: Generic or domain-specific intent labels are contextualized because their terse, inconsistent wording can be vague and ambiguous across datasets.Source labels typically contain only 1–3 words or use domain-specific jargon.
  • Stage 3: Task Format Unification: Heterogeneous source datasets are converted into a unified multiple-choice QA format requiring intent understanding of queries, dialogues, or monologues.Each instance includes a context, a question, answer options, correct intents, and metadata such as domain, text form, annotation type, and sensitivity level.
  • Stage 3: Task Format Unification: All Set options are capped at 10, with correct intents included and remaining options randomly drawn from the intent statement pool.The option count is defined as u = min{10, T}, and multiple correct intents are allowed when applicable.
  • IntentGrasp Data Splitting: Training and test splits inherit source-dataset partitions, while de-duplication and random test downsampling balance contributions across datasets.The All Set limit is no more than 500 instances per source dataset.

4 IntentGrasp Evaluation

IntentGrasp evaluates 20 models across seven families on All Set and Gem Set, using human and random-guess baselines as references. Results remain below 60% on All Set and below 25% on Gem Set, with domain- and instance-level analyses revealing further variation.

  • Evaluation Setup: 20 models spanning seven families answer IntentGrasp multiple-choice questions on All Set and Gem Set in a specified output format.The evaluated families include Llama3, Qwen3, Olmo3, Gemma4, GPT-5, Gemini-3, and Claude-4.
  • Baselines: 81.1% is the estimated human F1 baseline, while 15.2% is the random-guess F1 baseline.These baselines provide references for desirable and chance-level performance on IntentGrasp.
  • Overall Performance: Under 60% of models’ performance is observed on All Set, and under 25% on Gem Set.Figure 2 averages F1 scores over multiple runs and reports 2-sigma standard-deviation error bars.
  • Overall Performance: Gemma4-31B performs best on All Set among the open-source models, while open models generally underperform GPT-5, Gemini-3, and Claude-4.Qwen3 slightly surpasses the Llama3 and Olmo3 families among the four open-source model families.
  • Performance Breakdown by Domains: Writing, e-commerce, teaching, empathetic response, and customer support are more challenging domains than daily life and general for open-source models.Performance is described as largely consistent within each domain, and Gemma4 consistently outperforms the other open-source models in the reported breakdown.
  • Performance Breakdown by Instance Types: GPT models do not perform better on the synthetic data, indicating that IntentGrasp adequately reconstructed its six synthetic source datasets.The six synthetic source datasets mostly use GPT in their data-building pipeline.

5 IFT: Intentional Fine-Tuning

Intentional Fine-Tuning (IFT) fine-tunes Qwen3-4B and Qwen3-8B on IntentGrasp training data and substantially improves intent understanding over prompting and untrained baselines. Gains remain consistent across domains and extend to unseen domains under leave-one-domain-out training, although performance remains weak in news and policy making.

  • Method: IFT fine-tunes Qwen3-4B and Qwen3-8B on the full IntentGrasp training set, with additional experiments varying the training-data percentage.Models are evaluated on both All Set and Gem Set under the same settings as the prior evaluation.
  • Effectiveness: IFT significantly outperforms Direct Answer, Chain-of-Thought, and Intentional Analysis baselines on both IntentGrasp evaluation sets.Among baselines, CoT improves over DA, while IA yields further gains by explicitly triggering intentional analysis.
  • Domain-wise Performance: After IFT, All Set performance exceeds 85% in daily life and general, while Gem Set e-commerce reaches an F1 score of >65.5%.E-commerce is the only domain where the fine-tuned model performs better on Gem Set than on All Set.
  • Limitations: Despite consistent IFT gains, the model still struggles in news and policy making, with F1 scores of <15%.These results indicate continuing difficulty understanding intentions in those domains.
  • Cross-domain Generalizability: Leave-one-domain-out IFT consistently benefits unseen target domains, demonstrating strong cross-domain generalizability, especially for general, teaching, and coronavirus pandemic.The target domain is excluded during training and then used for evaluation.

6 Conclusion

IntentGrasp is introduced as a standardized multiple-choice QA benchmark for evaluating LLM intent understanding across diverse domains and instance types. The paper emphasizes that accurate intent understanding is important for preventing harmful consequences, particularly in high-stakes applications.

  • Benchmark: IntentGrasp evaluates intent understanding across 12 diverse domains and varying instance types, including text forms, annotation types, and sensitivity levels.It is designed as a standardized multiple-choice question-answering benchmark.
  • Benchmark: 12,909 test cases comprise the All Set, while 470 challenging cases comprise the Gem Set.The benchmark also provides a large-scale training set of 262,759 instances.
  • Broader Impacts: Accurate intent understanding is pivotal as LLMs assist people across diverse uses, because misunderstandings can cause harmful consequences.The concern is especially important in high-stakes tasks and areas such as healthcare, legal, and finance.

A Data Details · A.1 Statistics of IntentGrasp Evaluation Sets · A.2 Estimated Human Performance on Source Datasets

IntentGrasp’s evaluation comprises a 12,909-instance All Set and a challenging 470-instance Gem Set spanning 12 domains, with accompanying statistics on task composition and answer options. Human performance is estimated per source dataset using reported human scores or inter-annotator agreement proxies, alongside adopted instance counts.

  • A.1 Statistics of IntentGrasp Evaluation Sets: 12 diverse domains are covered, including daily life, smart assistant, toxic speech, writing, general, e-commerce, teaching, empathetic response, news, customer support, coronavirus pandemic, and policy making.The domains are abbreviated DL, SA, TS, W, G, EC, T, ER, N, CS, CP, and PM.
  • A.1 Statistics of IntentGrasp Evaluation Sets: 12,909 instances comprise All Set, while Gem Set contains 470 challenging instances.Gem Set is a challenging subset of All Set.
  • A.1 Statistics of IntentGrasp Evaluation Sets: All Set statistics include text forms, intent label types, annotation styles, sensitivity levels, and domains.These composition statistics are presented in Table 4.
  • A.1 Statistics of IntentGrasp Evaluation Sets: Multiple-choice QA is the evaluation format, with option and correct-intent-answer statistics reported separately.These statistics are provided in Table 5.
  • A.2 Estimated Human Performance on Source Datasets: Table 6 reports human performance or proxy scores for each source dataset and the number of adopted instances in IntentGrasp.The reported quantities are organized at the source-dataset level.
  • A.2 Estimated Human Performance on Source Datasets: AI-synthetic datasets use reported human performance when available, whereas human-annotated datasets use inter-annotator agreement as the human-performance proxy.The cited proxy measures include Cohen’s kappa, Fleiss’ kappa, and Krippendorff’s alpha references.

A.3 Estimated Random-Guess Performance on Source Datasets · B Experiment Details

The section defines a random-guess baseline for IntentGrasp and derives its instance-level F1 behavior from the number of correct answers and available options. It also identifies the quantities reported for human-performance estimates across source datasets.

  • A.3 Estimated Random-Guess Performance on Source Datasets: The random-guess baseline selects one option randomly for each test instance.Its performance is estimated for the IntentGrasp evaluation set.
  • A.3 Estimated Random-Guess Performance on Source Datasets: For each instance, the probability of selecting a correct answer is vi/ui, where vi counts correct answers and ui counts options.The evaluation set is represented as X = {xi}N_i=1.
  • A.3 Estimated Random-Guess Performance on Source Datasets: When a random guess is correct, precision is 1 and recall is 1/vi.These values determine the corresponding instance-level F1 score.
  • A.3 Estimated Random-Guess Performance on Source Datasets: A correct random guess receives an F1 score of 2/(1+vi), whereas an incorrect guess receives an F1 score of 0.The score depends on vi, the number of correct answers for the instance.
  • A.3 Estimated Random-Guess Performance on Source Datasets: The random-guess baseline’s overall F1 score is derived by aggregating these instance-level outcomes.The supplied derivation begins with the expression for the overall score but is truncated after “1”.
  • A.3 Estimated Random-Guess Performance on Source Datasets: Table 6 reports human performance or proxy scores for each source dataset.The table covers datasets adopted into IntentGrasp’s evaluation sets.
  • A.3 Estimated Random-Guess Performance on Source Datasets: Table 6 defines “# All” as the number of instances adopted in the All Set and “# Gem” as the number adopted in the Gem Set.These columns describe source-dataset coverage in the two evaluation sets.

B.1 Evaluation Experiment Settings

The evaluation uses 20 Transformer-based, decoder-only, instruction-following models from seven LLM families, with standardized deterministic generation settings. Models are tested through 0-shot generation without few-shot exemplars or external retrievals.

  • Models: 20 models from seven LLM families are evaluated, comprising open-source Llama3, Qwen3, Olmo3, and Gemma4 models and proprietary GPT-5, Gemini-3, and Claude-4 models.Open models are loaded from Hugging Face, while proprietary models are accessed through API calls.
  • Generation Details: Temperature 0, a 2,048-token generation limit, and random seed 42 define the default generation configuration.Large open-source models are run on a single NVIDIA A6000 GPU with 4-bit quantization.
  • Evaluation Details: 0-shot generation is used without few-shot exemplars or external retrievals, with prompts containing context, questions, and answer options.The prompting slot supports baseline variants including empty prompting for DA and step-by-step or intent-analysis prompts for CoT and IA.

B.2 Fine-tuning Experiment Settings

Intentional Fine-Tuning (IFT) uses supervised next-token prediction over straightforwardly constructed training instances, without additional prompting. The experiments apply fixed training defaults and select the final checkpoint using held-out validation performance.

  • Training Data: Training instances are constructed straightforwardly, with no additional CoT or IA prompting during fine-tuning, generation, or evaluation.The method is intended to preliminarily leverage IntentGrasp’s training set to improve intent understanding.
  • Training Objective: IFT is a supervised fine-tuning method for decoder-only LLMs that minimizes average cross-entropy loss for next-token prediction.The objective compares predicted-token distributions with the distributions of expected tokens.
  • Training Hyperparameters: Default training uses 1 epoch, batch size 8, maximum context length 4096, BF16 precision, random seeds 42, and AdamW optimization.AdamW uses beta1 0.9, beta2 0.999, epsilon 1e-8, and maximum gradient norm 1; WSD schedules the learning rate.
  • Validation for Model Selection: Model selection holds out 1% of training samples for validation, evaluates checkpoints every 10% of training steps, and selects the checkpoint with the best validation score.Validation uses the generation and evaluation settings specified in Appendix B.1.

B.3 Experimental Results … C.2 Ethics and Safeguards

The paper reports detailed IntentGrasp evaluations and performance breakdowns, alongside reproducibility practices and ethical safeguards for sensitive intent data. IFT produces consistent cross-domain gains, while the benchmark excludes personally identifiable information and addresses harmful content for safety evaluation.

  • B.3 Experimental Results: Evaluation reliability is strengthened by shuffling each instance’s option list three times with random seeds 7, 42, and 365.The procedure addresses potential sensitivity of LLMs to option order; the passage also notes downsampling for cost efficiency, though the excerpt is truncated.
  • B.3 Experimental Results: Detailed results are reported across evaluation sessions, domains, and instance types, including text forms, intent-label multiplicity, annotation source, and sensitivity.The domain breakdown covers 12 domains, while instance-type analyses distinguish queries, dialogue, monologue, single or multiple intents, AI-synthetic or human annotations, and harmful-content sensitivity.
  • C Further Discussions: Qwen3-4B and Qwen3-8B with IFT show significant overall gains and consistent improvements across all domains.The reported results use 100% of the training data across all domains; Figure 7 includes 2-sigma standard-deviation error bars for statistical significance.
  • C.1 Reproducibility Statement: Deterministic open-source-model generation uses temperature 0 with token sampling disabled, and generation experiments are conducted at least twice for reproducibility.The authors also manage package versions, random seeds, and other hyperparameters, and release the dataset on Hugging Face and source code on GitHub.
  • C.1 Reproducibility Statement: Open-source experiments run on single NVIDIA A6000 or V100 GPUs using Ubuntu 22.04.5 LTS and a Python 3.10 Miniconda environment.Required package versions are specified in the code documents, and experiment runtime varies by configuration.
  • C.2 Ethics and Safeguards: The study states compliance with the NeurIPS Code of Ethics and reports that IntentGrasp contains no personally identifiable information.The benchmark is manually constructed by the authors without crowdsourcing or research involving human subjects; source datasets remain available for use.
  • C.2 Ethics and Safeguards: Sensitive datasets cover toxic, abusive, hateful, and disinformation-related content to evaluate models’ understanding of malicious intents for improved safety.The authors include such data because accurate malicious-intent understanding is important for safety evaluation, while the excerpt’s remaining safeguards statement is truncated.

C.3 Limitations and Future Work

IntentGrasp’s scope is limited by its text-only, English-only design, which does not capture multimodal or cross-cultural variation in intent understanding. Its static release may also be vulnerable to data contamination, whereas dynamic benchmarks can mitigate test-data leakage risks.

  • Research Scope: IntentGrasp uses text-only datasets, excluding multimodal aspects of humans’ intent comprehension and expression.The paper relates multimodal understanding of the world to intent comprehension and expression.
  • Research Scope: The benchmark covers English-only text, although different cultures and languages may understand intent differently.The authors identify cross-linguistic and cross-cultural intent understanding as an unaddressed dimension.
  • Static Benchmark: Static benchmarks may suffer from data contamination because determining whether test instances leaked into training data is challenging.IntentGrasp releases its training set and two evaluation sets, creating the stated contamination concern.
  • Static Benchmark: Dynamic benchmarks can mitigate the risk of test-data leakage, but they introduce challenges in benchmark provision.The supplied passage indicates a trade-off between leakage mitigation and dynamic-benchmark construction.
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