Bi-Fact: A Bidirectional Factorization-based Evaluation of Intent Extraction from UI Trajectories
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912259775660032 |
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| author | Caduri, Sapir Efros, Anatoly Kahlon, Noam Cohen, Danielle Halpern, Yoni Dagan, Ido |
| author_facet | Caduri, Sapir Efros, Anatoly Kahlon, Noam Cohen, Danielle Halpern, Yoni Dagan, Ido |
| contents | Evaluating intent extraction from GUIs demands accurate, fine-grained metrics. This paper introduces Bi-Fact, a novel method that decomposes intents into atomic facts and performs bidirectional comparisons to assess precision and recall. Experiments demonstrate Bi-Fact's superior correlation with human judgments compared to existing metrics, establishing a more robust evaluation framework for UI-driven intent understanding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13149 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bi-Fact: A Bidirectional Factorization-based Evaluation of Intent Extraction from UI Trajectories Caduri, Sapir Efros, Anatoly Kahlon, Noam Cohen, Danielle Halpern, Yoni Dagan, Ido Artificial Intelligence Evaluating intent extraction from GUIs demands accurate, fine-grained metrics. This paper introduces Bi-Fact, a novel method that decomposes intents into atomic facts and performs bidirectional comparisons to assess precision and recall. Experiments demonstrate Bi-Fact's superior correlation with human judgments compared to existing metrics, establishing a more robust evaluation framework for UI-driven intent understanding. |
| title | Bi-Fact: A Bidirectional Factorization-based Evaluation of Intent Extraction from UI Trajectories |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2502.13149 |