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Generative large language models (LLMs), e.g., ChatGPT, have demonstratedremarkable proficiency across several NLP tasks, such as machine translation,text summarization. Recent research (Kocmi and Federmann, 2023) has shown thatutilizing ChatGPT for assessing the quality of machine translation (MT)achieves state-of-the-art performance at the system level but performs poorlyat the segment level. To further improve the performance of LLMs on MT qualityassessment, we conduct an investigation into several prompting methods, andpropose a new prompting method called Error Analysis Prompting (EAPrompt) bycombining Chain-of-Thoughts (Wei et al., 2022) and Error Analysis (Lu et al.,2022). Our results on WMT22 indicate that prompting LLMs like ChatGPT witherror analysis can generate human-like MT evaluations at both the system andsegment level. Additionally, we first discover some limitations of ChatGPT asan MT evaluator, such as changing the order of input may significantlyinfluence the judgment when providing multiple translations in a single query.This work provides a preliminary experience of prompting LLMs as an evaluatorto improve the reliability of translation evaluation metrics under the erroranalysis paradigm.

Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models: A Case Study on ChatGPT