Overview of the Causal World Model architecture.
Large Language Models (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.
Large Language Models (LLMs) have emerged as powerful tools for a wide range of tasks, from natural language understanding to complex problem-solving. Recent work has explored the use of LLMs as action agents for planning and reasoning tasks, showing promising results in improving task-specific, downstream performance. However, these approaches primarily rely on the model's ability to extract common-sense causal information stated in its training data.
While LLMs can reflect general beliefs and correlations, this information may be incomplete, incorrect, or inapplicable in specific environments. This poses challenges for LLMs in novel or complex situations, particularly in dynamic environments where accurate modeling of action consequences is crucial. To address these limitations, we propose integrating Causal Representation Learning (CRL) with LLMs to create a more robust and adaptable system for reasoning and planning.
Our framework combines the strengths of Large Language Models with Causal World Models (CWMs) to create a powerful system for causally-aware reasoning and planning. This integration addresses the critical limitation of traditional LLMs: their struggle to understand true causal relationships in complex environments.
By learning a structured, causal world model from the environment, we provide a foundation for the LLM to reason about actions, their consequences, and long-term planning. This causal grounding allows the system to make more informed decisions, especially in scenarios where understanding the underlying causal structure is crucial.
We leverage existing causal representation learning methods and build around them a language encoder and a decoder to enable an interface between the causal world model and natural language, allowing the LLM to reason about the world in a causal manner.
Overview of the Causal World Model architecture.
At the heart of our framework is a multi-component architecture that creates a causal world model:
1. Causal Encoder: This component maps high-dimensional state representations, such as images, to fundamental causal variables. It distills complex environmental states into their essential causal components, providing a structured representation for reasoning.
2. Causal Transition Model: This is the engine that simulates the next state based on the current state and action, leveraging learned causal mechanisms. It enables the system to reason about the consequences of actions and interventions, allowing for accurate prediction and planning.
3. Decoder: The decoder transforms the causal variables back into natural language descriptions. It consists of two parts: the causal mapper, which extracts interpretable causal variables from the learned disentangled representations, and the state description generator, which converts these variables into human-readable text.
This architecture allows for a structured, causal understanding of the environment, which can then be leveraged by an LLM for more effective planning and reasoning across various domains and complexities.
Overview of a single rollout in the Causal LLM planning pipeline.
Causal LLM excels in two key areas: causal inference and planning. For causal inference, the system can reason about the effects of interventions and counterfactuals. It can answer questions like "What would happen if we performed action X?" by simulating the intervention in its causal world model. This capability allows for more robust decision-making in complex environments.
In planning tasks, as demonstrated both in the figure above and in the video at the top of this page, Causal LLM employs a modified Monte Carlo Tree Search (MCTS) algorithm that leverages the causal world model. This approach allows the system to evaluate multiple possible futures before taking action, reason about the causal consequences of different action sequences, and make more informed decisions based on its causal understanding. The result is a planning system that can handle longer horizons and more complex scenarios than traditional LLM-based planners.
Our experiments demonstrate that Causal LLM consistently outperforms traditional LLM-based reasoners, especially in scenarios involving longer planning horizons and complex, dynamic environments. As the planning horizon increases, Causal LLM maintains higher accuracy compared to baseline LLMs, highlighting its ability to reason over longer causal chains.
While our current implementation focuses on relatively simple environments, the framework is designed to extend to more complex scenarios as CRL methods advance. Future work includes scaling to more complex, real-world environments, improving the interpretability of learned causal world models, and developing techniques that are independent of labeled causal variables.
@article{gkountouras2023language,
title={Language Agents Meet Causality -- Bridging LLMs and Causal World Models},
author={Gkountouras, John and Lindemann, Matthias and Lippe, Phillip and Gavves, Efstratios and Titov, Ivan},
journal={arXiv preprint arXiv:XXXX.XXXXX},
year={2023}
}