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Found 6 papers, 3 papers with code
Causal Parrots: Large Language Models May Talk Causality But Are Not Causal
We conjecture that in the cases where LLM succeed in doing causal inference, underlying was a respective meta SCM that exposed correlations between causal facts in natural language on whose data the LLM was ultimately trained.
Continual Causal Abstractions
This short paper discusses continually updated causal abstractions as a potential direction of future research.
Pearl Causal Hierarchy on Image Data: Intricacies & Challenges
Many researchers have voiced their support towards Pearl's counterfactual theory of causation as a stepping stone for AI/ML research's ultimate goal of intelligent systems.
Can Foundation Models Talk Causality?
Foundation models are subject to an ongoing heated debate, leaving open the question of progress towards AGI and dividing the community into two camps: the ones who see the arguably impressive results as evidence to the scaling hypothesis, and the others who are worried about the lack of interpretability and reasoning capabilities.
The Causal Loss: Driving Correlation to Imply Causation
Most algorithms in classical and contemporary machine learning focus on correlation-based dependence between features to drive performance.
Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data
Crazyhouse is a game with a higher branching factor than chess and there is only limited data of lower quality available compared to AlphaGo.
