Semiquantitative in-context causal reasoning for LLMs - Thielo, Marcelo R.; Rocha, Bernardo N. (Anais da Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS); 2025: Anais da I Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região SulDO - 10.5753/eramiars.2025.16634, 2025)
We propose a novel algorithm that employs simulations of causal dynamical models and fuzzy logic to push forward the ability of large language models(LLMs) on extracting networks of causal relations from text documents. As a means to that, we explored the ability of LLMs to emulate actual critical reasoning and self-criticize on their own conclusions based on simulations of the gradually improving causal models, which we called In-Context Reasoning. As the self-assessment strategy, an instance of LLM-as-a-judge was employed. The results are encouraging, and we expect to further contribute to the investigations of the potential of LLMs in activities that require more accurate logical and structured reasoning.