Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents (2411.06559v1)

Abstract: 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 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.

Model-Based Planning for Web Agents: A Critical Evaluation of LLMs as World Models

The paper entitled "Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents" explores the novel concept of enhancing language agents with model-based planning using LLMs as world models in web environments. The core proposition is the WebDreamer framework, which leverages the inherent ability of LLMs to encode comprehensive knowledge about web structures and functionalities, thus facilitating efficient planning and decision-making processes for web-based tasks.

Technical Approach: WebDreamer

WebDreamer employs model-based planning by utilizing LLMs to simulate potential outcomes of various actions that web agents may execute. The framework operates on the premise that LLMs, trained extensively on web data, inherently possess world models that could predict the results of interactions within internet environments. By preemptively simulating actions, WebDreamer aims to avoid the hazards associated with executing irreversible actions on live websites.

The technical foundation of WebDreamer lies in a model predictive control (MPC)-like strategy, where an LLM is tasked with (i) simulating the website's state changes induced by potential actions and (ii) scoring these simulations to guide action selection. This approach allows for informed decision-making without actual interaction, maintaining a safety buffer against potential negative consequences of real-time web modifications.

Empirical Findings

The empirical evaluation of WebDreamer was conducted on two benchmarks: VisualWebArena (VWA) and Mind2Web-live. The results exhibit substantial performance enhancements over baseline reactive approaches, with significant improvements in success rates—33.3% on VWA, demonstrating WebDreamer's practical advantage. However, the framework fell short of tree search approaches in controlled settings due to the simplicity of the planning algorithm, indicating room for methodological refinements.

Theoretical and Practical Implications

The implications of using LLMs as world models are vast. Theoretically, this paradigm supports the hypothesis that LLMs hold latent capabilities to simulate complex web interactions, thus functioning as ad-hoc planners. Practically, the approach provides a potentially safer and more efficient mode of automating web navigation tasks by minimizing direct interactions, which can pose irreversible consequences.

Challenges and Limitations

Despite the promising results, the paper notes key limitations, such as the computational costs involved with current AI models like GPT-4o and the challenges of long-horizon planning given the propensity for state change simulation to become less accurate with extended use. Furthermore, due to the reliance on extensive simulations, the method demands parallel computations to mitigate latency, which impacts scalability and real-time applicability.

Future Directions

This pioneering work encourages further exploration of LLMs in simulating diverse and real-world environments with greater fidelity. A critical avenue for future research lies in fine-tuning LLMs for enhanced world model capabilities, coupled with the development of more advanced planning algorithms like MCTS to extend WebDreamer's efficacy to complex, multi-step tasks. Additionally, applying these concepts to more varied and unpredictable web tasks could broaden the utility of LLM-based web agents significantly.

Conclusion

WebDreamer offers a compelling demonstration of LLMs augmenting model-based planning in web environments. This paper initiates a dialog on the capabilities of LLMs beyond conventional NLP tasks, suggesting their potential in dynamic, real-world applications. While the path forward presents technical challenges, this direction promises to enrich AI's involvement in interactive and autonomous decision systems.