Computational conceptual history of scientific concepts: From early digital methods to LLMs

A new paper situates large language models within the longer history of computational approaches to analyzing scientific concepts in the history, philosophy, and sociology of science. The study reconstructs computational conceptual history before LLMs, examining early digital methods, distributional approaches, and lexical semantic change detection, then reviews how LLMs inherit longstanding problems related to corpus construction, model choice, and evaluation. The findings highlight how methodological trade-offs and interpretation challenges persist in LLM-based workflows for studying conceptual change in science.

arXiv:2606.04118v1 Announce Type: new 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.