  Automatically evaluating the quality of language generation is critical.
Although recent learned metrics show high correlation with human judgement,
these metrics can not explain their verdict or associate the scores with
defects in generated text. To address this limitation, we present
InstructScore, an explainable evaluation metric for text generation. By
harnessing both explicit human instruction and the implicit knowledge of GPT-4,
we fine-tune a text evaluation metric based on LLaMA, producing both a score
for generated text and a human readable diagnostic report. We evaluate
InstructScore on a variety of generation tasks, including translation,
captioning, data-to-text and commonsense generation. Experiments show that our
7B model surpasses all other unsupervised metrics, including those based on
175B GPT-3 and GPT-4. Surprisingly, our InstructScore, even without direct
supervision from human-rated data, achieves performance levels on par with
state-of-the-art metrics like COMET22, which were fine-tuned on human ratings.
