Bi-Fact: A Bidirectional Factorization-based Evaluation of Intent Extraction from UI Trajectories
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 20 references that are in the map. First posted on arXiv 11 Feb 2025.
Builds on
9 of 20 references.
References · 9
- 2015A large annotated corpus for learning natural language inferencecited by 1,847
- 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
- 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,242
- 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 463
- 2024OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesiscited by 163
- 2024Aria-UI: Visual Grounding for GUI Instructionscited by 140
- 2023Automatic Macro Mining from Interaction Traces at Scalecited by 12
- 2024Identifying User Goals from UI Trajectoriescited by 9
- 2024SummAct: Uncovering User Intentions Through Interactive Behaviour Summarisationcited by 9
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Citing papers per year: 2025: 2.
2 papers.