Human Label Variation (HLV)

Cross-Annotator Preference Optimization (CAPO) for Learning Annotator-Specific Explanation Behavior

This paper investigates whether LLMs can learn and reproduce individual annotator-specific reasoning patterns, not just label choices, using two sentence-pair tasks (NLI and paraphrase judgment) with four annotators each. The authors find that annotator-specific patterns are weak at the single-annotation level but detectable after aggregation, and propose CAPO—a preference optimization method that contrasts a target annotator's response against other valid but less target-specific annotations. CAPO outperforms prompting and supervised fine-tuning baselines in capturing annotator-specific label-explanation behavior. The work suggests a path toward scalable annotation pipelines grounded in annotator histories rather than labels alone.