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CGCE: A Chinese Generative Chat Evaluation Benchmark for General and Financial Domains

Authors :
Zhang, Xuanyu
Li, Bingbing
Yang, Qing
Publication Year :
2023

Abstract

Generative chat models, such as ChatGPT and GPT-4, have revolutionized natural language generation (NLG) by incorporating instructions and human feedback to achieve significant performance improvements. However, the lack of standardized evaluation benchmarks for chat models, particularly for Chinese and domain-specific models, hinders their assessment and progress. To address this gap, we introduce the Chinese Generative Chat Evaluation (CGCE) benchmark, focusing on general and financial domains. The CGCE benchmark encompasses diverse tasks, including 200 questions in the general domain and 150 specific professional questions in the financial domain. Manual scoring evaluates factors such as accuracy, coherence, expression clarity, and completeness. The CGCE benchmark provides researchers with a standardized framework to assess and compare Chinese generative chat models, fostering advancements in NLG research.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2305.14471
Document Type :
Working Paper