How does varying the complexity of Structural Causal Models in causal fine-tuning affect the OOD F1 score stab

The causal capabilities of large language models (LLMs) are a matter of significant debate, with critical implications for the use of LLMs in societally impactful domains such as medicine, science, law, and policy. We conduct a "behavorial" study of LLMs to benchmark their capability in generating causal arguments. Across a wide range of tasks, we find that LLMs can generate text corresponding to correct causal arguments with high probability, surpassing the best-performing existing methods. Algorithms based on GPT-3.5 and 4 outperform existing algorithms on a pairwise causal discovery task (9

Research goal: How does varying the complexity of Structural Causal Models in causal fine-tuning affect the OOD F1 score stability of tabular foundation models on benchmarks like TabFact?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.1/10.