  Recent advancements in large language models (LLMs) on language modeling and
emergent capabilities make them a promising reference-free evaluator of natural
language generation quality, and a competent alternative to human evaluation.
However, hindered by the closed-source or high computational demand to host and
tune, there is a lack of practice to further calibrate an off-the-shelf
LLM-based evaluator towards better human alignment. In this work, we propose
AutoCalibrate, a multi-stage, gradient-free approach to automatically calibrate
and align an LLM-based evaluator toward human preference. Instead of explicitly
modeling human preferences, we first implicitly encompass them within a set of
human labels. Then, an initial set of scoring criteria is drafted by the
language model itself, leveraging in-context learning on different few-shot
examples. To further calibrate this set of criteria, we select the best
performers and re-draft them with self-refinement. Our experiments on multiple
text quality evaluation datasets illustrate a significant improvement in
correlation with expert evaluation through calibration. Our comprehensive
qualitative analysis conveys insightful intuitions and observations on the
essence of effective scoring criteria.
