CoAScore: Chain-of-Aspects Prompting for NLG Evaluation

Understand

Recently, natural language generation (NLG) evaluation has shifted from a single-aspect to a multi-aspect paradigm, allowing for a more accurate assessment.

• Large language models (LLMs) achieve superior performance on various NLG evaluation tasks.
• However, current work often employs the LLM to independently evaluate different aspects, which largely ignores the rich correlation between various aspects.
• To fill this research gap, in this work, we propose an NLG evaluation metric called CoAScore.