大型语言模型在政治话语中的文化适应
Cultural Adaptation in Large Language Models for Political Discourse
AI总结：
本文探讨大型语言模型在政治话语分析中的文化适应问题，提出通过翻译、话语和本体层面的形式化框架及评估矩阵，确保跨文化部署的可靠性和民主安全性。
摘要：
大型语言模型融入政治话语分析为比较研究、政策分析和公民技术创造了新机遇，同时也为民主问责带来了实质性风险。本文认为，文化适应是在不同语言和制度背景下将大型语言模型可靠地部署于政治传播的先决条件。当前系统仍受英语主导数据、不均衡的多语言覆盖以及基于狭窄范围的政治制度和话语惯例的假设所影响，导致跨文化应用时产生系统性错误。我们在翻译、话语和本体层面形式化文化适应，识别政治自然语言处理中反复出现的文化失败模式，并提出一个基于文化保真度、校准度和民主安全性的操作性评估矩阵。基于政治文本分析、社会技术审计和跨文化语用学，我们概述了方法论路径，包括参与式数据集开发、文化感知迁移学习以及使文化适应可经验测量的基准设计。最后，我们阐明了适应性政治自然语言处理能够支持民主合法性的治理约束和适用范围条件。
英文摘要：
The integration of large language models into political discourse analysis creates new opportunities for comparative research, policy analysis, and civic technology, while introducing material risks for democratic accountability. This paper argues that cultural adaptation is a prerequisite for trustworthy deployment of large language models in political communication across diverse linguistic and institutional contexts. Current systems remain shaped by English dominant data, uneven multilingual coverage, and assumptions grounded in a narrow range of political institutions and discourse conventions, producing systematic errors when applied across cultures. We formalize cultural adaptation across translation, discourse, and ontology levels, identify recurring cultural failure modes in political NLP, and propose an operational evaluation matrix grounded in cultural fidelity, calibration, and democratic safety. Building on political text analysis, sociotechnical auditing, and cross cultural pragmatics, we outline methodological pathways including participatory dataset development, culturally aware transfer learning, and benchmark design that makes cultural adaptation empirically measurable. We conclude by clarifying governance constraints and scope conditions under which culturally adaptive political NLP can support democratic legitimacy.
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。
