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Existing LLM-as-a-Judge approaches for evaluating text generation suffer from rating inconsistencies, with low agreement and high rating variance across different evaluator models.
Language models are few-shot learners
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Inter-annotator agreement
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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