  Automatic methods for evaluating machine-generated texts hold significant
importance due to the expanding applications of generative systems.
Conventional methods tend to grapple with a lack of explainability, issuing a
solitary numerical score to signify the assessment outcome. Recent advancements
have sought to mitigate this limitation by incorporating large language models
(LLMs) to offer more detailed error analyses, yet their applicability remains
constrained, particularly in industrial contexts where comprehensive error
coverage and swift detection are paramount. To alleviate these challenges, we
introduce DEE, a Dual-stage Explainable Evaluation method for estimating the
quality of text generation. Built upon Llama 2, DEE follows a dual-stage
principle guided by stage-specific instructions to perform efficient
identification of errors in generated texts in the initial stage and
subsequently delves into providing comprehensive diagnostic reports in the
second stage. DEE is fine-tuned on our elaborately assembled dataset AntEval,
which encompasses 15K examples from 4 real-world applications of Alipay that
employ generative systems. The dataset concerns newly emerged issues like
hallucination and toxicity, thereby broadening the scope of DEE's evaluation
criteria. Experimental results affirm that DEE's superiority over existing
evaluation methods, achieving significant improvements in both human
correlation as well as efficiency.
