Toward Responsible and Epistemically Grounded Multilingual LLMs for Computational Social Science and Humanities
For computational social science and humanities researchers, it addresses the lack of epistemically grounded evaluation of multilingual LLMs in interpretive tasks, but remains theoretical without empirical validation.
The paper reconceptualizes multilingual LLMs as hermeneutic instruments for Social Sciences and Humanities, developing a theoretical framework and experimental protocol with metrics for cultural alignment, cross-lingual stability, and reasoning faithfulness. It provides a conceptual foundation but no empirical results.
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.