Computational conceptual history of scientific concepts: From early digital methods to LLMs
This article situates large language models (LLMs) within the longer history of computational approaches to concept analysis in the history, philosophy, and sociology of science (HPSS). It examines what LLMs add to existing methods, how they inherit longstanding problems, and reviews recent case studies. The first part reconstructs computational conceptual history before LLMs, covering early digital methods, distributional approaches, and lexical semantic change detection. The second part focuses on the LLM era, reviewing LLM-based work on lexical semantic change detection and relevant case studies, and revisiting methodological issues such as corpus construction, model choice, and operationalization.
[2606.04118] Computational conceptual history of scientific concepts: From early digital methods to LLMs
[Submitted on 2 Jun 2026]
Title:Computational conceptual history of scientific concepts: From early digital methods to LLMs
View a PDF of the paper titled Computational conceptual history of scientific concepts: From early digital methods to LLMs, by Michael Zichert and 1 other authors
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Abstract:This article situates large language models (LLMs) within the longer history of computational approaches to concept analysis in the history, philosophy, and sociology of science (HPSS). We examine what LLMs add to existing methods, how they inherit longstanding problems, and review recent case studies that employ them. In the first part, we reconstruct computational conceptual history before LLMs by bringing together three strands of work: early digital methods in HPSS, distributional approaches from digital history and related research, and lexical semantic change detection. We provide an overview of the main challenges and opportunities, focusing on corpus construction, operationalization and modelling choices, and evaluation and interpretation. In the second part, we turn to the era of LLMs, starting with a short introduction to LLMs before reviewing LLM-based work on lexical semantic change detection and relevant case studies in HPSS. We then revisit the earlier methodological questions, showing how issues of corpus construction, model choice and training data, operationalization trade-offs, and evaluation and interpretation play out in LLM-based workflows.
Comments: 19 pages, chapter in the book Understanding Science with Large Language Models? (pp. 383-412). transcript. Edited by Arno Simons, Adrian Wüthrich, Michael Zichert, Gerd Graßhoff (eds.)
Subjects:
Computation and Language (cs.CL)
Report number: ISBN: 978-3-8376-7994-6
Cite as: arXiv:2606.04118 [cs.CL]
(or arXiv:2606.04118v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.04118
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Michael Zichert [view email] [v1] Tue, 2 Jun 2026 18:28:29 UTC (275 KB)
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