Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.13149 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912259775660032 |
|---|---|
| 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 |