HeaP: Hierarchical Policies for Web Actions using LLMs
Paloma Sodhi S. R. K. Branavan Ryan McDonald
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 them via a sequence
of 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.