Retrieval-augmented generation (RAG) systems can produce convincing predictions while ignoring retrieved evidence. Researchers at RWTH Aachen University propose a training recipe designed to make evidence causally necessary for each decision. In their paper, a case-grounded verifier performs strongly with correct evidence and falls close to random ranking when that evidence is swapped, giving developers a direct way to detect shortcut learning.

Why plausible answers go ungrounded

Many RAG pipelines append retrieved evidence to the input and supervise only the final prediction. That objective rewards a correct answer regardless of whether the model used the evidence, relied on patterns in the local case, or matched keywords shared by the claim and passage.

Standard evaluations usually leave the evidence fixed, so aggregate accuracy cannot reveal which signal drove the decision. The RWTH formulation instead scores a triple containing a local case, a structured claim, and an external evidence unit. For a radiology concept such as cardiomegaly, the claim specifies whether the condition is present or absent, and the verifier decides whether a Radiopaedia passage supports that state for the case.

Four labels close the shortcuts

The training pipeline partitions external evidence into concept-specific pools and combines each passage with cases and claim polarities. Its four example types expose the model to support, contradiction, topical distraction, and unrelated content: