And there's a great paper by @_FelixSimon_ and Sacha Altay on the actual impact of generative AI on elections. And more that I'll be writing about later, (incl great art from Seb Krier).
Read them all at buff.ly/zTrRirO. Thanks so much to @knightcolumbia (and especially Katy) for making it happen
![Computer Science > Artificial Intelligence
arXiv:2411.06559 (cs)
[Submitted on 10 Nov 2024]
Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents
Yu Gu, Boyuan Zheng, Boyu Gou, Kai Zhang, Cheng Chang, Sanjari Srivastava, Yanan Xie, Peng Qi, Huan Sun, Yu Su
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Language agents have demonstrated promising capabilities in automating web-based tasks, though their current reactive approaches still underperform largely compared to humans. While incorporating advanced planning algorithms, particularly tree search methods, could enhance these agents' performance, implementing tree search directly on live websites poses significant safety risks and practical constraints due to irreversible actions such as confirming a purchase. In this paper, we introduce a novel paradigm that augments language agents with model-based planning, pioneering the innovative use of large language models (LLMs) as world models in complex web environments. Our method, WebDreamer, builds on the key insight that LLMs inherently encode comprehensive knowledge about website structures and functionalities. Specifically, WebDreamer uses LLMs to simulate outcomes for each candidate action (e.g., "what would happen if I click this button?") using natural language descriptions, and then evaluates these imagined outcomes to determine the optimal action at each step. Empirical results on two representative web agent benchmarks with online interaction -- VisualWebArena and Mind2Web-live -- demonstrate that WebDreamer achieves substantial improvements over reactive baselines. By establishing the viability of LLMs as world models in web environments, this work lays the groundwork for a paradigm shift in automated web interaction. More broadly, our findings open exciting new avenues for future research into 1) optimizing LLMs specifically for world modeling in complex, dynamic environments, and 2) model-based speculative planning for language agents.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:552sltcbj2n32vqfg2zkjnyj/bafkreic6rrtzqqnmrg3sfqloujvm36zszohuis4bzrpy4zkug5mrzwx4dm)
