Shashank Aggarwal, Ram Vikas Mishra, Amit Awekar
This paper introduces reusability and verifiability as new metrics to evaluate the quality of Chain-of-Thought reasoning in multi-agent IR pipelines, revealing that these metrics are not correlated with traditional accuracy measures.
In the world of AI and machine learning, agents often need to reason and communicate with each other to accomplish tasks like searching and ranking information. Traditionally, the success of these tasks has been measured by how accurately the final goal is achieved. However, this doesn't tell us much about the quality of the reasoning process itself. This paper proposes two new ways to evaluate reasoning: reusability, which looks at how easily one agent can use another's reasoning, and verifiability, which checks how often one agent can arrive at the same conclusion using shared reasoning. The study finds that these new measures don't always align with traditional accuracy, suggesting that current evaluation methods might be missing important aspects of reasoning quality.
