LLMs fine-tuned for HTML, allowing them to summarize verbose HTML code [388] in real-world
scenarios and extract valuable information. Furthermore, WebGum [390] empowers agents with
visual perception abilities by employing a multimodal corpus containing HTML screenshots. It
simultaneously fine-tunes the LLM and a visual encoder, deepening the agent’s comprehensive
understanding of web pages.
TheRiseandPotentialofLargeLanguageModel BasedAgents