Causal Cartographer: From Mapping to Reasoning Over Counterfactual Worlds
We introduce a retrieval-augmented system for causal extraction and representating causal knowledge, and a methodology for provably estimating real-world counterfactuals. We show that causal-guided step-by-step LRMs can achieve competitive performance while greatly reducing LLMs' context and output length, decreasing inference cost up to 70%.
Counterfactual Causal Inference in Natural Language
We build the first causal extraction and counterfactual causal inference system for natural language, and propose a new direction for model oversight and strategic foresight.
Independent Causal Language Models
We develop a novel modular language model architecture sparating inference into independant causal modules, and show that it can be used to improve abstract reasoning performance and robustness for out-of-distribution settings.
Behaviour Modelling of Social Agents
We model the behaviour of interacting social agents (e.g. meerkats) using a combination of causal inference and graph neural networks, and demonstrate increased efficiency and interpretability compared to existing architectures.
Large Language Models Are Not Strong Abstract Reasoners
We evaluate the performance of large language models on abstract reasoning tasks and show that they fail to adapt to unseen reasoning chains, highlighting a lack of generalization and robustness.
Disentanglement via Causal Interventions on a Quantized Latent Space
We propose a new approach to disentanglement based on hard causal interventions over a quantized latent space, and demonstrate its potential for improving the interpretability and robustness of generative models.