Toward Responsible and Epistemically Grounded Multilingual LLMs for Computational Social Science and Humanities
arXiv:2606.00596v1 Announce Type: new Abstract: 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
The rapid evolution of multilingual LLMs has brought their integration into diverse research fields, making discussions about their responsible application and interpretative validity crucial now.
This paper redefines the role of LLMs as hermeneutic instruments, highlighting the critical need to address their cultural situatedness and epistemic mediation for credible research in social sciences and humanities.
The focus for evaluating LLMs is shifting beyond task-based NLP benchmarks to include deeper considerations of interpretive validity and cultural context, challenging the prevailing assessment paradigms.
- · Ethical AI researchers
- · Social Sciences and Humanities
- · Developers of culturally aware LLMs
- · Developers of uncritical LLM evaluation methods
- · Research relying on uncontextualized LLM output
- · Monolingual LLM development
Increased emphasis will be placed on embedding cultural and linguistic context directly into LLM architectures and training data.
This shift could foster the development of 'culturally intelligent' AI systems, leading to more nuanced and reliable cross-cultural communication and research applications.
Long-term, this could enable new forms of digitally-mediated cross-cultural understanding and collaboration, while also exposing new forms of algorithmic bias previously overlooked.
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