  As research in large language models (LLMs) continues to accelerate,
LLM-based evaluation has emerged as a scalable and cost-effective alternative
to human evaluations for comparing the ever increasing list of models. This
paper investigates the efficacy of these ``LLM evaluators'', particularly in
using them to assess instruction following, a metric that gauges how closely
generated text adheres to the given instruction. We introduce a challenging
meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM
evaluator in discerning instruction-following outputs. The authors manually
curated 419 pairs of outputs, one adhering to instructions while the other
diverging, yet may possess deceptive qualities that mislead an LLM evaluator,
e.g., a more engaging tone. Contrary to existing meta-evaluation, we discover
that different evaluators (i.e., combinations of LLMs and prompts) exhibit
distinct performance on LLMBar and even the highest-scoring ones have
substantial room for improvement. We also present a novel suite of prompting
strategies that further close the gap between LLM and human evaluators. With
LLMBar, we hope to offer more insight into LLM evaluators and foster future
research in developing better instruction-following models.
