BaRDa: A Belief and Reasoning Dataset that Separates Factual Accuracy and Reasoning Ability
This provides a new benchmark for researchers to more cleanly separate and quantify factual accuracy and reasoning ability in language models, addressing a key evaluation bottleneck.
The paper tackles the problem of conflating factual accuracy and reasoning ability in language model evaluations by creating BaRDa, a dataset with 3000 entailments using true and false statements, and finds that GPT-series models show improved scores, e.g., GPT-4 achieving 87.1% factual accuracy and 79.2% reasoning accuracy.
While there are numerous benchmarks comparing the performance of modern language models (LMs), end-task evaluations often conflate notions of *factual accuracy* ("truth") and *reasoning ability* ("rationality", or "honesty" in the sense of correctly reporting implications of beliefs). Our goal is a dataset that clearly distinguishes these two notions. Our approach is to leverage and extend a collection of human-annotated *entailment trees*, engineered to express both good and bad chains of reasoning, and using a mixture of true and false facts, in particular including counterfactual examples, to avoid belief bias (also known as the "content effect"). The resulting dataset, called BaRDa, contains 3000 entailments (1787 valid, 1213 invalid), using 6681 true and 2319 false statements. Testing on four GPT-series models, GPT3(curie)/GPT3(davinici)/3.5/4, we find factual accuracy (truth) scores of 74.1/80.6/82.6/87.1 and reasoning accuracy scores of 63.1/78.0/71.8/79.2. This shows the clear progression of models towards improved factual accuracy and entailment reasoning, and the dataset provides a new benchmark that more cleanly separates and quantifies these two notions.