Do language models understand the causal structure of the world, or do they merely note correlations? And what happens when you build a big AI society out of them? In this brief episode, recorded at the Bay Area Alignment Workshop, I chat with Zhijing Jin about her research on these questions.

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The transcript: https://axrp.net/episode/2024/11/14/episode-38_0-zhijing-jin-llms-causality-multi-agent-systems.html

FAR.AI: https://far.ai/

FAR.AI on X (aka Twitter): https://x.com/farairesearch

FAR.AI on YouTube: https://www.youtube.com/@FARAIResearch

The Alignment Workshop: https://www.alignment-workshop.com/

Topics we discuss, and timestamps:

00:35 - How the Alignment Workshop is

00:47 - How Zhijing got interested in causality and natural language processing

03:14 - Causality and alignment

06:21 - Causality without randomness

10:07 - Causal abstraction

11:42 - Why LLM causal reasoning?

13:20 - Understanding LLM causal reasoning

16:33 - Multi-agent systems

Links:

Zhijing's website: https://zhijing-jin.com/fantasy/

Zhijing on X (aka Twitter): https://x.com/zhijingjin

Can Large Language Models Infer Causation from Correlation?: https://arxiv.org/abs/2306.05836

Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents: https://arxiv.org/abs/2404.16698

Episode art by Hamish Doodles: hamishdoodles.com