Summary of Identifying User Goals From Ui Trajectories, by Omri Berkovitch et al.
Identifying User Goals from UI Trajectories
by Omri Berkovitch, Sapir Caduri, Noam Kahlon, Anatoly Efros, Avi Caciularu, Ido Dagan
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a new task called “task goal identification” that aims to infer a user’s detailed intentions when performing a task within a user interface (UI) environment. To support this task, the authors introduce a novel evaluation methodology designed to assess whether two intent descriptions can be considered paraphrases within a specific UI environment. The proposed task leverages datasets designed for the inverse problem of UI automation, utilizing Android and web datasets for experiments. The performance of state-of-the-art models, such as GPT-4 and Gemini-1.5 Pro, is compared to that of humans using a proposed metric. Results reveal that both Gemini and GPT underperform relative to human performance, highlighting the challenge of the proposed task and the significant room for improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about understanding what people want to do when they use computers or smartphones. The researchers want to find out why users are doing certain things, like searching online or playing games. They propose a new way to figure this out by looking at how people interact with user interfaces (like screens and menus). To test their idea, they used special datasets and compared the results of computers and humans. Unfortunately, the computers didn’t do very well, so there’s still more work to be done. |
Keywords
» Artificial intelligence » Gemini » Gpt
