Language Agents Meet Causality -- Bridging LLMs and Causal World Models (2410.19923v1)

Abstract: LLMs have recently shown great promise in planning and reasoning applications. These tasks demand robust systems, which arguably require a causal understanding of the environment. While LLMs can acquire and reflect common sense causal knowledge from their pretraining data, this information is often incomplete, incorrect, or inapplicable to a specific environment. In contrast, causal representation learning (CRL) focuses on identifying the underlying causal structure within a given environment. We propose a framework that integrates CRLs with LLMs to enable causally-aware reasoning and planning. This framework learns a causal world model, with causal variables linked to natural language expressions. This mapping provides LLMs with a flexible interface to process and generate descriptions of actions and states in text form. Effectively, the causal world model acts as a simulator that the LLM can query and interact with. We evaluate the framework on causal inference and planning tasks across temporal scales and environmental complexities. Our experiments demonstrate the effectiveness of the approach, with the causally-aware method outperforming LLM-based reasoners, especially for longer planning horizons.

• The paper introduces a framework that integrates LLMs with causal representation learning to enhance reasoning and planning in virtual environments.
• It employs a causal world model with specialized components like causal encoders and text-based action representations to disentangle complex settings.
• Empirical evaluations in gridworld and 3D-rendered kitchens demonstrate superior data efficiency and multistep planning over baseline LLMs.

Bridging LLMs and Causal World Models through Integrated Frameworks

The paper "Language Agents Meet Causality -- Bridging LLMs and Causal World Models" introduces a novel framework that integrates LLMs with causal representation learning (CRL) to enhance reasoning and planning capabilities in virtual environments. This synthesis aims to exploit the strengths of both paradigms, enabling LLMs to effectively utilize causal structures for improved decision-making and planning.

Enhanced Understanding through Causal Representation Learning

The paper begins by acknowledging the proficiency of LLMs in various complex tasks, including planning and reasoning, yet highlights a critical gap in their causal understanding capabilities. LLMs, while rich in commonsense causal knowledge derived from vast pre-trained data, often exhibit limitations when required to apply this knowledge in specific, dynamic contexts. To address this, the proposed framework introduces causal representation learning, which focuses on identifying and modeling causal structures inherent to a particular environment. By utilizing CRL, the framework effectively disentangles complex environments into more manageable causal variables, thereby bridging the gap between abstract language capabilities and concrete causal reasoning.

Framework Overview and Methodology

The core innovation of the proposed system lies in its integration mechanism, wherein the LLM is connected to a world model constructed from CRL. This causal world model acts as an intermediary, or simulator, allowing the LLM to interact with and query its causal constructs via natural language. The authors provide a detailed account of the framework's architecture, introducing key components like a causal encoder, causal mapper, and text-based action representations, each contributing to a comprehensive understanding of the environment through causal lenses.

Text-based action representations, an important feature of this framework, offer a flexible method for encoding interventions in natural language, enhancing both the interpretability and applicability of CRL across different domains. Notably, the framework demonstrates superior data efficiency in learning causal representations, with experiments suggesting a marked improvement over traditional methods in data-scarce scenarios.

Empirical Evaluation and Performance

The empirical evaluation of the framework is conducted across two separate environments: a dynamic 8x8 gridworld and a static 3D-rendered kitchen (AI2-THOR). The performance results indicate that the combination of causal world models with LLMs significantly outperforms a baseline LLM, particularly in tasks requiring multistep planning and causal inference over longer temporal horizons. Notably, in the gridworld environment, the causal world model maintains high accuracy even over extended planning sequences, showcasing its robustness in dynamic settings.

Implications and Future Directions

The integration of CRLs with LLMs outlined in the paper has notable implications for both AI research and applications. Practically, this framework could enhance the robustness and reliability of AI systems in real-time decision-making and planning tasks. Theoretically, it provides insights into how explicit causal understanding can be used to improve general reasoning capabilities of language agents.

Looking forward, the paper suggests that as CRL methodologies advance, their framework could be scaled to more complex real-world environments. Future research directions may explore methods to further automate the mapping of causal variables to human-interpretable formats and to refine the framework's applicability to real-world dynamics beyond simulated environments.

In summary, this paper presents a significant step towards creating AI systems that blend the strengths of linguistic and causal reasoning. By equipping LLMs with a causal understanding of their environment, the framework not only advances AI's capabilities in simulated tasks but also opens up possibilities for real-world applications where causal reasoning is essential.