What is it about?
Question-answering (QA) systems are widely used in healthcare, finance, and research, but they often produce unreliable or incorrect answers. Traditional evaluation methods, like exact match or F1 scores, don’t fully assess their performance. This research introduces a new framework that uses commonsense reasoning and Chain-of-Thought (CoT) reasoning to evaluate QA systems more effectively. By combining CoT reasoning with a GPT model, the framework improves the reliability of answers, even when they don’t perfectly match the expected results. Tests on the SQuAD 2.0 dataset show significant improvements in handling answers and increasing trust in QA systems.
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Why is it important?
QA systems are critical for decision-making in high-stakes fields like healthcare and finance, but their current limitations, such as generating incorrect or nonsensical answers can have serious consequences. This work addresses these gaps by providing a robust and interpretable evaluation framework. It not only enhances the reliability of QA systems but also offers transparency, making it easier to understand why an answer is correct or incorrect. This advancement ensures that QA systems can be trusted for real-world applications where accuracy is paramount.
Perspectives
While working on this research, we realized how challenging it is to make neural networks reason like humans. Large language models (LLMs) excel at probabilistic pattern recognition but often lack true logical reasoning; they generate answers based on statistical likelihood rather than structured, human-like deduction. This limitation became a driving force behind our work, pushing us to develop a framework that bridges the gap between raw model outputs and reliable, interpretable reasoning. I hope this research sparks further discussion on how to make AI systems not just statistically accurate but also logically sound. If we can better evaluate and refine reasoning in QA systems, we move closer to AI that users can truly trust, especially in critical fields like healthcare and finance. Beyond technical improvements, this work reminds us that AI, for all its power, still struggles with something innate to humans: the ability to reason clearly. That’s both a humbling and exciting challenge for future research.
Dr. Sanjay Singh
Manipal Institute of Technology, Manipal
Read the Original
This page is a summary of: Chain-of-Thought Reasoning Evaluation Framework for Question Answering System, February 2025, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/aide64228.2025.10987492.
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