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Large language models (LLMs) that have been trained on a corpus that includeslarge amount of code exhibit a remarkable ability to understand HTML code. Asweb interfaces are primarily constructed using HTML, we design an in-depthstudy to see how LLMs can be used to retrieve and locate important elements fora user given query (i.e. task description) in a web interface. In contrast withprior works, which primarily focused on autonomous web navigation, we decomposethe problem as an even atomic operation - Can LLMs identify the importantinformation in the web page for a user given query? This decomposition enablesus to scrutinize the current capabilities of LLMs and uncover the opportunitiesand challenges they present. Our empirical experiments show that while LLMsexhibit a reasonable level of performance in retrieving important UI elements,there is still a substantial room for improvement. We hope our investigationwill inspire follow-up works in overcoming the current challenges in thisdomain.

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