Are LLMs Good at Causal Reasoning? with Robert Osazuwa Ness - #638
Analysis
This podcast episode from Practical AI delves into the capabilities of Large Language Models (LLMs) in causal reasoning. The discussion centers around evaluating models like GPT-3, 3.5, and 4, highlighting their limitations in answering causal questions. The guest, Robert Osazuwa Ness, emphasizes the need for access to model weights, training data, and architecture for accurate causal analysis. The episode also touches upon the challenges of generalization in causal relationships, the importance of inductive biases, and the role of causal factors in decision-making. The focus is on understanding the current state and future potential of LLMs in this complex area.
Key Takeaways
- •LLMs, like GPT models, are evaluated for their causal reasoning abilities.
- •Limitations exist in current LLMs' ability to answer specific causal questions.
- •Access to model details (weights, data, architecture) is crucial for improvement.
“Robert highlights the need for access to weights, training data, and architecture to correctly answer these questions.”