Large language models for history, philosophy, and sociology of science

Interpretive uses, methodological challenges, and critical perspectives

Bibliographic Data

ID10799028
AuthorsArno Simons (0000-0003-0657-5254, Technische Universität Berlin, corresponding author), Michael Zichert (0009-0007-8575-5750, Technische Universität Berlin), Adrian Wüthrich (0000-0002-6237-7327, Technische Universität Berlin)
Year2026
Volume117
Pages102151
Publication date2026-06-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueStudies in History and Philosophy of Science (JOURNAL)
Journal identifiersISSN: 0039-3681 • E-ISSN: 1879-2510
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.shpsa.2026.102151
PMID41916166
OpenAlexW7143442326
LanguageEN
References cited43

This paper examines large language models (LLMs) as research tools in the history, philosophy, and sociology of science (HPSS). Because LLMs can work directly with heterogeneous, unstructured texts and capture meaning-relevant associations from usage patterns, they offer new ways to bridge close reading and corpus-scale analysis, challenging the idea that computational scale and interpretive nuance must trade off. We provide a compact primer on LLMs, covering the main components of their neural network architecture, the differences between generative and full-context models, and adaptation strategies such as fine-tuning, prompt-based learning, and retrieval-augmented generation (RAG). Building on this foundation, we analyze how LLMs recast three classic methodological problems in HPSS: working with historically messy data, detecting and interpreting large-scale patterns, and modeling scientific change over time. Across these areas we synthesize recent work in HPSS and adjacent fields, and we clarify how LLM outputs can function as exploratory prompts, as inputs to more structured pipelines, or as evidence under stricter validation and documentation. We conclude with four lessons: 1) model choice embeds interpretive trade-offs, 2) responsible use requires LLM literacy, 3) HPSS should develop its own tasks and evaluation practices, and 4) LLMs should extend rather than replace established interpretive methods. We also situate these methodological questions within broader concerns about platform dependence, accountability, and the responsibilities attached to research infrastructures. Finally, we argue that HPSS is well positioned to both use LLMs and to interrogate what counts as explanation, evidence, and responsible use in interpretive research

Field (mathematics · Identification (biology · Interpretation (philosophy · On Language · Perspective (graphical · Computational and Text Analysis Methods · Language and cultural evolution · Philosophy and History of Science

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