{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-real-world-webagent-with-planning-long","title":"A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis","arxiv_id":"2307.12856","date":"2023-07-24","proceeding":null,"authors":["Izzeddin Gur","Hiroki Furuta","Austin Huang","Mustafa Safdari","Yutaka Matsuo","Douglas Eck","Aleksandra Faust"],"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.","url_abs":"https://arxiv.org/abs/2307.12856v4","url_pdf":"https://arxiv.org/pdf/2307.12856v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"code-generation","task_name":"Code Generation"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"inductive-bias","task_name":"Inductive Bias"},{"task_slug":"long-context-understanding","task_name":"Long-Context Understanding"},{"task_slug":"program-synthesis","task_name":"Program Synthesis"},{"task_slug":"task-planning","task_name":"Task Planning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/on-mind2web","task":"","dataset":"Mind2Web","model":"HTML-T5-XL","rank_in_archive_order":1,"of":1,"metrics":{"Element Accuracy":"73","Operation F1 score":"75.6","Step Success Rate":"67.1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2307.12856","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}