CGCE：面向通用与金融领域的中文生成式聊天评估基准
CGCE: A Chinese Generative Chat Evaluation Benchmark for General and Financial Domains
AI总结：
针对中文聊天模型缺乏标准化评估基准的问题，本文提出了涵盖通用与金融领域的CGCE基准，通过200个通用和150个金融专业问题及人工多维评分，为中文生成式聊天模型提供标准化评估框架。
摘要：
生成式聊天模型（如ChatGPT和GPT-4）通过引入指令和人类反馈实现了显著的性能提升，彻底改变了自然语言生成（NLG）。然而，聊天模型缺乏标准化评估基准，尤其是针对中文和特定领域模型，这阻碍了其评估与进展。为填补这一空白，我们引入了中文生成式聊天评估（CGCE）基准，聚焦通用与金融领域。该基准包含多样化任务，涵盖200个通用领域问题和150个金融领域专业问题。人工评分评估准确性、连贯性、表达清晰度及完整性。CGCE基准为研究人员提供了评估和比较中文生成式聊天模型的标准化框架，推动了NLG研究的发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
