Bi-Fact: A Bidirectional Factorization-based Evaluation of Intent Extraction from UI Trajectories

Sapir Caduri, Anatoly Efros, Noam Kahlon, Danielle Cohen, Yoni Halpern +1 more
2/11/2025
cs.AI

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Abstract

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.

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Cite this paper

@article{caduri2025bifact,
  title  = {Bi-Fact: A Bidirectional Factorization-based Evaluation of Intent Extraction from UI Trajectories},
  author = {Sapir Caduri and Anatoly Efros and Noam Kahlon and Danielle Cohen and Yoni Halpern and Ido Dagan},
  year   = {2025},
  eprint = {2502.13149},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.13149v3}
}

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