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Can Foundation Models Talk Causality?
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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.
Forward citations
Cited by 2 Pith papers
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Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery
A learning-to-rank model chooses informative knowledge-graph paths between entity pairs, and adding the top path to zero-shot prompts improves LLM causal classification by up to 44.4 F1 points.
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Position: Foundation Models Need Digital Twin Representations
A position paper proposes replacing token-based representations in foundation models with outcome-driven digital twin representations that explicitly encode physical and semantic structure.
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