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20192026
most citedCausal Parrots: Large Language Models May Talk Causality But Are Not Causal

27 citations · 48 across the 14 of their papers we have counts for

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7 papers · 1 filter

cs.AI2025

Fodor and Pylyshyn's Legacy: Still No Human-like Systematic Compositionality in Neural Networks

Tim Woydt, Moritz Willig, Antonia Wüst +4

Strong meta-learning capabilities for systematic compositionality are emerging as an important skill for navigating the complex and changing tasks of today's world. However, in pre…

cs.AI2023

Do Not Marginalize Mechanisms, Rather Consolidate!

Moritz Willig, Matej Zečević, Devendra Singh Dhami +1

Structural causal models (SCMs) are a powerful tool for understanding the complex causal relationships that underlie many real-world systems. As these systems grow in size, the num…

cs.AI2023★ 27 cited

Causal Parrots: Large Language Models May Talk Causality But Are Not Causal

Matej Zečević, Moritz Willig, Devendra Singh Dhami +1

Some argue scale is all what is needed to achieve AI, covering even causal models. We make it clear that large language models (LLMs) cannot be causal and give reason onto why some…

cs.AI2023

Continual Causal Abstractions

Matej Zečević, Moritz Willig, Jonas Seng +1

This short paper discusses continually updated causal abstractions as a potential direction of future research. The key idea is to revise the existing level of causal abstraction t…

cs.AI2022

Pearl Causal Hierarchy on Image Data: Intricacies & Challenges

Matej Zečević, Moritz Willig, Devendra Singh Dhami +1

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. As in a…

cs.AI2022★ 8 cited

Can Foundation Models Talk Causality?

Moritz Willig, Matej Zečević, Devendra Singh Dhami +1

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 arguabl…

cs.AI2019★ 13 cited

Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data

Johannes Czech, Moritz Willig, Alena Beyer +2

Deep neural networks have been successfully applied in learning the board games Go, chess and shogi without prior knowledge by making use of reinforcement learning. Although starti…