Can Foundation Models Talk Causality?
This work addresses the philosophical divide in the AI community regarding the capabilities of foundation models, but it appears incremental as it explores an existing concern without claiming major breakthroughs.
The paper investigates whether large-scale language models can capture causal representations, aiming to address debates about their progress towards AGI and interpretability issues.
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. By investigating to which extent causal representations might be captured by these large scale language models, we make a humble efforts towards resolving the ongoing philosophical conflicts.