A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis

Published: 16 Jan 2024, Last Modified: 14 Mar 2024ICLR 2024 oralEveryoneRevisionsBibTeX
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Keywords: Web Navigation, Web Automation, Large Language Models, Language Model Agents, Tool Use, Program Synthesis
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TL;DR: We propose a modular language model agents for real-world web automation by leveraging the capability of multi-step planning, long context understanding, and program synthesis in LLMs.
Abstract: Pre-trained large language models (LLMs) have recently achieved better generalization and sample efficiency in autonomous web automation. However, the performance on real-world websites has still suffered from (1) open domainness, (2) limited context length, and (3) lack of inductive bias on HTML. We introduce WebAgent, an LLM-driven agent that learns from self-experience to complete tasks on real websites following natural language instructions. WebAgent plans ahead by decomposing instructions into canonical sub-instructions, summarizes long HTML documents into task-relevant snippets, and acts on websites via Python programs generated from those. We design WebAgent with Flan-U-PaLM, for grounded code generation, and HTML-T5, new pre-trained LLMs for long HTML documents using local and global attention mechanisms and a mixture of long-span denoising objectives, for planning and summarization. We empirically demonstrate that our modular recipe improves the success on real websites by over 50%, and that HTML-T5 is the best model to solve various HTML understanding tasks; achieving 18.7% higher success rate than the prior method on MiniWoB web automation benchmark, and SoTA performance on Mind2Web, an offline task planning evaluation.
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Primary Area: applications to robotics, autonomy, planning
Submission Number: 1265