Chain-of-Thought Reasoning Evaluation Framework for Question Answering System

Question-answering (QA) systems have become crucial in healthcare, decision-making, finance, and research domains. While transformer-based language models have greatly enhanced the generation of contextually relevant answers, they often struggle to provide reliable answers, leading to hallucinations and incorrect predictions. Traditional evaluation metrics, such as exact match, precision, recall, and F1 metric, demonstrate significant limitations in comprehensively assessing the performance and capabilities of QA systems. This research introduces a commonsense reasoning-based evaluation framework for language models, leveraging the Chain-of-Thought (CoT) reasoning approach. Our proposed method integrates CoT reasoning with a GPT o1-mini model to evaluate the predictions from language models. Experiments conducted on the SQuAD 2.0 dataset demonstrate significant improvements in handling the answers generated by the language models and increase the reliability of the QA system. The framework offers robust and interpretable solutions, addressing critical gaps in current QA systems and ensuring reliability and transparency for practical, real-world applications.