An Exploration of Causal Cognition in Large Language Models
Abstract
Causal cognition, how beings perceive and reason about cause and effect, is crucial not only for survival and adaptation in biological entities but also for the development of causal artificial intelligence. Large language models (LLMs) have recently taken center stage due to their remarkable capabilities, demonstrating human-like reasoning in their generative responses. This thesis explores how LLMs perform on causal reasoning questions and how modifying information in the prompt affect their reasoning. Using 1392 causal inference questions from the CLADDER dataset, LLM responses were assessed for accuracy. With simple prompting, LLMs performed more accurately on intervention queries compared to association or counterfactual queries. Chain-of-Thought (CoT) prompting was also explored with formal reasoning steps included in the prompts. Contrary to expectations, LLMs achieved higher accuracy with simple prompts rather than CoT-enhanced prompts, suggesting that the framework for accurate causal cognition in LLMs differs from that of human cognition.