#AAAI2024 workshops round-up 2: AI for credible elections, and are large language models simply causal parrots?

Speakers presented various perspectives on large language models (LLMs) in the context of causality and symbolic reasoning. Emre Kıcıman (Microsoft Research) emphasized that LLMs can be useful in the applied causal process, even if they don't have fully generalizable causal capabilities. Andrew Lampinen (Google DeepMind) shared the insights from his work, suggesting that LLMs can learn generalizable causal strategies under certain circumstances, but these circumstances are likely not met for the existing models. Guy van den Broeck (UCLA) presented his work on constraining and conditioning LLM generation using hidden Markov models (HMMs). Judea Pearl shared his thoughts on the possibility of LLMs learning a partial implicit world model.