Measuring Quality of General Reasoning
Abstract
Machine learning models that automatically assess reasoning quality are trained on human-annotated written products. These “gold-standard” corpora are typically created by prompting annotators to choose, using a forced choice design, which of two products presented side by side is the most convincing, contains the strongest evidence or would be adopted by more people. Despite the increase in popularity of using a forced choice design for assessing quality of reasoning (QoR), no study to date has established the validity and reliability of such a method. In two studies, we simultaneously presented two products of reasoning to participants and asked them to identify which product was ‘better justified’ through a forced choice design. We investigated the criterion validity and inter-rater reliability of the forced choice protocol by assessing the relationship between QoR, measured using the forced choice protocol, and accuracy in objectively answerable problems using naive raters sampled from MTurk (Study 1) and experts (Study 2), respectively. In both studies products that were closer to the correct answer and products generated by larger teams were consistently preferred. Experts were substantially better at picking the reasoning products that corresponded to accurate answers. Perhaps the most surprising finding was just how rapidly raters made judgements regarding reasoning: On average, both novices and experts made reliable decisions in under 15 seconds. We conclude that forced choice is a valid and reliable method of assessing QoR.