Large Language Models and Causal Reasoning

The podcast episode explores the topic of large language models and their potential for causal reasoning. The guest, Robert Ness, discusses a recent paper on causal reasoning and large language models, highlighting the implications and challenges in this field. The models were tested on benchmarks for pairwise causal discovery and full-graph causal discovery, showing promising results. However, they also had limitations and vulnerabilities to prompt manipulation. The episode raises questions about the models' ability to generalize, use domain knowledge, and understand context. It also suggests the potential of reinforcement learning to train models on specific causal tasks. Robert mentions interesting work by Atticus Geiger on interleaved intervention training. Finally, Robert shares his reading recommendation on a book about parenting and causal inference by Emily Oster.