Recent studies show that neural retrievers often exhibit source bias, ranking LLM-generated passages above semantically similar human-written ones, which threatens the reliability of modern information access systems. While this bias is often viewed as a model-level property, we show that relevance supervision itself can induce it. Empirically, source bias is clearest in relevance-supervised retrievers, and the same encoders become more LLM-preferring after relevance fine-tuning. We trace this effect to surface-level artifacts in supervised retrieval data: annotated positives and negatives differ systematically in properties such as fluency and lexical specificity, and these differences parallel those between LLM-generated and human-written passages. As a result, retrievers can learn artifact-based shortcuts for relevance. We further show theoretically that, under contrastive learning, artifacts that reliably separate positives from negatives can be encoded into learned relevance scores. Motivated by this mechanism, we propose complementary strategies that reduce source bias during training and at inference: balancing artifact distributions in negative sampling and removing the artifact-aligned embedding component, respectively. Overall, our findings support a data-centric view of source bias: retrievers may prefer LLM-generated text not merely because of inherent model properties, but because relevance supervision teaches them to rely on surface-level artifacts.

翻译：暂无翻译