Causal-aware Large Language Models: Enhancing Decision-Making Through Learning, Adapting and Acting
This addresses the challenge of applying LLMs to complex real-world tasks by enhancing their decision-making through causal reasoning, though it is incremental as it builds on existing LLM and causal modeling techniques.
The paper tackles the problem of large language models lacking reasoning abilities and adaptability in decision-making by proposing Causal-aware LLMs that integrate structural causal models into a learning-adapting-acting paradigm, achieving validated effectiveness across 22 diverse tasks in the open-world game 'Crafter'.
Large language models (LLMs) have shown great potential in decision-making due to the vast amount of knowledge stored within the models. However, these pre-trained models are prone to lack reasoning abilities and are difficult to adapt to new environments, further hindering their application to complex real-world tasks. To address these challenges, inspired by the human cognitive process, we propose Causal-aware LLMs, which integrate the structural causal model (SCM) into the decision-making process to model, update, and utilize structured knowledge of the environment in a ``learning-adapting-acting" paradigm. Specifically, in the learning stage, we first utilize an LLM to extract the environment-specific causal entities and their causal relations to initialize a structured causal model of the environment. Subsequently,in the adapting stage, we update the structured causal model through external feedback about the environment, via an idea of causal intervention. Finally, in the acting stage, Causal-aware LLMs exploit structured causal knowledge for more efficient policy-making through the reinforcement learning agent. The above processes are performed iteratively to learn causal knowledge, ultimately enabling the causal-aware LLMs to achieve a more accurate understanding of the environment and make more efficient decisions. Experimental results across 22 diverse tasks within the open-world game ``Crafter" validate the effectiveness of our proposed method.