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 has enabled highly flexible%2C reference-free metrics for various tasks%2C making LLM evaluators common tools in NLP. However%2C the robustness of these LLM evaluators remains relatively understudied%3B existing work mainly pursued optimal performance in terms of correlating LLM scores with human expert scores. In this paper%2C we conduct a series of analyses using the SummEval dataset and confirm that LLMs are biased evaluators as they: (1) exhibit familiarity bias-a preference for text with lower perplexity%2C (2) show skewed and biased distributions of ratings%2C and (3) experience anchoring effects for multi-attribute judgments. We also found that LLMs are inconsistent evaluators%2C showing low inter-sample agreement and sensitivity to prompt differences that are insignificant to human understanding of text quality. Furthermore%2C we share recipes for configuring LLM evaluators to mitigate these limitations. Experimental results on the RoSE dataset demonstrate improvements over the state-of-the-art LLM evaluators.?quality=80&w=800)
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