  The zero-shot capability of Large Language Models (LLMs) has enabled highly
flexible, reference-free metrics for various tasks, making LLM evaluators
common tools in NLP. However, the robustness of these LLM evaluators remains
relatively understudied; existing work mainly pursued optimal performance in
terms of correlating LLM scores with human expert scores. In this paper, 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, (2) show skewed and biased distributions of
ratings, and (3) experience anchoring effects for multi-attribute judgments. We
also found that LLMs are inconsistent evaluators, showing low "inter-sample"
agreement and sensitivity to prompt differences that are insignificant to human
understanding of text quality. Furthermore, 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.
