"What's important here?": Opportunities and Challenges of Using LLMs in Retrieving Information from Web Interfaces

• URL: http://arxiv.org/abs/2312.06147v1
• Date: Mon, 11 Dec 2023 06:26:38 GMT
• Title: "What's important here?": Opportunities and Challenges of Using LLMs in Retrieving Information from Web Interfaces
• Authors: Faria Huq, Jeffrey P. Bigham, Nikolas Martelaro
• Abstract summary: We study how large language models (LLMs) can be used to retrieve and locate important elements for a user given query in a web interface. Our empirical experiments show that while LLMs exhibit a reasonable level of performance in retrieving important UI elements, there is still a substantial room for improvement.
• Score: 19.656406003275713
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) that have been trained on a corpus that includes large amount of code exhibit a remarkable ability to understand HTML code. As web interfaces are primarily constructed using HTML, we design an in-depth study to see how LLMs can be used to retrieve and locate important elements for a user given query (i.e. task description) in a web interface. In contrast with prior works, which primarily focused on autonomous web navigation, we decompose the problem as an even atomic operation - Can LLMs identify the important information in the web page for a user given query? This decomposition enables us to scrutinize the current capabilities of LLMs and uncover the opportunities and challenges they present. Our empirical experiments show that while LLMs exhibit a reasonable level of performance in retrieving important UI elements, there is still a substantial room for improvement. We hope our investigation will inspire follow-up works in overcoming the current challenges in this domain.

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