生成式人工智能应用于编校工作的探索与分析——基于ChatGPT和150余款国产大模型的实测
Exploration and analysis of generative artificial intelligence in editing and proofreading:Based on practical tests of ChatGPT and over 150 Chinese large models
夏丽云 1岳于佳 2徐敏赟 2丁懿楠 3代建华2
作者信息
- 1. 湖南师范大学期刊社,湖南省长沙市岳麓区麓山南路36号 410081;智能计算与语言信息处理湖南省重点实验室,湖南省长沙市岳麓区麓山南路36号 410081
- 2. 智能计算与语言信息处理湖南省重点实验室,湖南省长沙市岳麓区麓山南路36号 410081
- 3. 北京外国语大学国际关系学院《国际论坛》编辑部,北京市海淀区西三环北路2号 100089
- 折叠
摘要
[目的]系统梳理和测试当前互联网中可获取和使用的大模型在编校方面的表现,明晰现有大模型的强项与不足,为编辑人员选用大模型进行编校提供参考,为推动模型编校能力发展提供依据.[方法]设计不同类型、不同难度等级差错文本,综合使用文本对比法、比较分析法、统计分析法等方法,根据模型回答的结果判断其准确率与稳定性.[结果]发现已有58款大模型具备编校能力;展示36款大模型处理不同类型和不同难度文本时的表现;归纳大模型在测试中表现出来的不足;国产大模型与ChatGPT相比具有优势.[结论]在实践层面,编辑可以选择合适的模型辅助编校工作;建立知识库,探索个性化的模型编校方式;使用角色设定和思维链询问方法以提高效率;进一步提升信息素养和专业技能.
Abstract
[Purposes]This study systematically reviews and tests the performance of currently available large models on the internet in terms of editing and proofreading.It aims to clarify the strengths and weaknesses of the existing large models,provide references for editors choosing large models for editing and proofreading,and offer a basis for advancing the development of these models'capabilities of editing and proofreading.[Methods]Different types of texts with varying complexity levels of error were designed to evaluate the accuracy and stability of the models'responses.Methods such as text comparison,comparative analysis,and statistical analysis were comprehensively employed.[Findings]58 models demonstrate editing and proofreading capabilities.The study showcases the performance of 36 models when handling different types and levels of textual complexity,summarizes the shortcomings observed during testing,and shows that the Chinese models have comparative advantages over ChatGPT.[Conclusions]In practical aspects,editors can select appropriate models to assist with their tasks,establish knowledge bases and personalized model-based editing and proofreading methods,use role setting and chain of thought inquiry methods to improve efficiency,and further enhance their information literacy and professional skills.
关键词
大模型/人工智能/编辑校对/实测Key words
Large model/Artificial intelligence/Editing and proofreading/Practical test引用本文复制引用
基金项目
北京外国语大学中央高校基本科研业务费专项(2021JS003)
出版年
2024
NETL
NSTL
万方数据