IDEAlign uses a pick-the-odd-one-out triplet task to measure idea-level similarity between LLMs and expert human annotations, and shows LLM judges using this protocol outperform lexical and vector-based baselines.
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IDEAlign: Comparing Large Language Models to Human Experts in Open-ended Interpretive Annotations
IDEAlign uses a pick-the-odd-one-out triplet task to measure idea-level similarity between LLMs and expert human annotations, and shows LLM judges using this protocol outperform lexical and vector-based baselines.