Presentation description

Previous research has shown increased large language model (LLM) accuracy with "Chain-of-Thought" (CoT) reasoning, using causality-based prompting for Q&A. By fine-tuning the T5 model emphasizing non-causal prompting using "This is what I know about this: {Explanation}. My answer would be {Answer}," and the reverse, the study aims to assess whether the noncausal prompt fine-tuning models maintains the model's reasoning performance and explanation reliability in the same way CoT would.