DEE: Dual-stage Explainable Evaluation Method for Text Generation

arXiv:2403.11509v1 Announce Type: new
Abstract: 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. …

abstract applications arxiv assessment cs.cl evaluation explainability generated generative importance machine numerical stage systems text text generation type