AllTogether: Investigating the Efficacy of Spliced Prompt for Web Navigation using Large Language Models

Understand

Large Language Models (LLMs) have emerged as promising agents for web navigation tasks, interpreting objectives and interacting with web pages.

• However, the efficiency of spliced prompts for such tasks remains underexplored.
• We introduces AllTogether, a standardized prompt template that enhances task context representation, thereby improving LLMs' performance in HTML-based web navigation.
• We evaluate the efficacy of this approach through prompt learning and instruction finetuning based on open-source Llama-2 and API-accessible GPT models.