面向负责任且基于认识论的多语言大语言模型在计算社会科学与人文学科中的应用
Toward Responsible and Epistemically Grounded Multilingual LLMs for Computational Social Science and Humanities
AI总结：
本文重新概念化多语言推理大语言模型为解释学工具，提出一个基于理论框架来评估其在社会科学与人文学科研究中的文化对齐、跨语言稳定性和推理忠实性。
摘要：
大语言模型在多语言能力和推理能力方面迅速发展，使其能够整合到社会科学与人文学科研究流程中。然而，现有的评估范式仍然基于任务型NLP基准，未能解决解释有效性、文化情境性和认识论中介问题。本文重新概念化多语言推理大语言模型为解释学工具，这些工具在不同语言和文化背景下积极构建意义生产。借鉴解释学、技术哲学、科学技术研究、多语言NLP研究和计算社会科学方法论，我们为评估社会科学与人文学科研究中的多语言推理开发了一个理论基础的框架。我们阐述了一个严格的实验协议，包含文化对齐、跨语言稳定性和推理忠实性的可操作化指标，以及针对解释性研究任务定制的透明度要求。我们通过一个涉及多语言政治话语分析的具体应用场景来说明该框架。本文为将多语言推理大语言模型负责任地整合到计算社会科学基础设施中提供了概念和方法论基础。
英文摘要：
Large language models have rapidly evolved in multilingual competence and reasoning capacity, enabling their integration into Social Sciences and Humanities research workflows. Yet existing evaluation paradigms remain anchored in task-based NLP benchmarks and fail to address interpretive validity, cultural situatedness, and epistemic mediation. This paper reconceptualizes multilingual reasoning LLMs as hermeneutic instruments that actively structure meaning production across linguistic and cultural contexts. Drawing on hermeneutics, philosophy of technology, science and technology studies, multilingual NLP research, and computational social science methodology, we develop a theoretically grounded framework for evaluating multilingual reasoning in Social Sciences and Humanities (SSH) research. We articulate a rigorous experimental protocol with operationalized metrics for cultural alignment, cross-lingual stability, and reasoning faithfulness, along with transparency requirements tailored to interpretive research tasks. We illustrate the framework through a concrete application scenario involving multilingual political discourse analysis. The paper contributes a conceptual and methodological foundation for responsible integration of multilingual reasoning LLMs into computational social science infrastructures.
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。
