HeaP: Hierarchical Policies for Web Actions using LLMs
Primary Area: applications to robotics, autonomy, planning
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Keywords: web actions, large language models, task decomposition, few-shot demonstrations
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TL;DR: LLMs that learn to solve complex web tasks by decomposing them into low-level policy calls, achieving superior performance with significantly less data
Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in performing a range of instruction-following tasks in few and zero-shot settings. However, teaching LLMs to perform tasks on the web presents fundamental challenges -- combinatorially large open-world tasks and variations across web interfaces. We tackle these challenges by leveraging LLMs to decompose web tasks into a collection of sub-tasks, each of which can be solved by a low-level, closed-loop policy. These policies constitute a shared grammar across tasks, i.e., new web tasks can be expressed as a composition of these policies. We propose a novel framework, Hierarchical Policies for Web Actions using LLMs (HeaP), that learns a set of hierarchical LLM prompts from demonstrations for planning high-level tasks and executing low-level policies. We evaluate HeaP against a range of baselines on a suite of web tasks, including MiniWoB++, WebArena, a mock airline CRM, as well as live website interactions, and show that it is able to outperform prior works using orders of magnitude less data.
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Submission Number: 6350
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