Speaker
Péter Antal
(Budapest University of Technology and Economics)
Description
Large language models (LLMs) face challenges with complex
probabilistic, causal, and counterfactual reasoning, yet they
demonstrate promising capabilities in the automated construction of both
qualitative and quantitative probabilistic and causal models. This talk
provides an overview of current benchmarks for causal reasoning in LLMs,
methods for extracting knowledge from LLMs, fundamental limitations in
LLM inference processes, and recent approaches that integrate LLMs with
causal inference technologies.
