DERELAB: Probing Defeasible Reasoning and Confirmation Bias in LLMs with a Generative Benchmark
DeReLab is introduced, a generative framework that produces multi-turn belief-updating conversations from parameterized graph structures across default and inheritance reasoning, with formally verified ground truth at every turn, enabling controlled measurement of how models respond to confirming and disconfirming evid...