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 lexi
The proliferation and increasing capabilities of Large Language Models (LLMs) necessitate a critical evaluation of their role in analytical fields like the history and philosophy of science.
This development highlights the ongoing integration of advanced AI into academic research and underscores the changing methodologies for understanding complex historical and conceptual data, impacting how knowledge itself is constructed and interpreted.
Traditional computational approaches to conceptual history are evolving, with LLMs introducing new analytical possibilities while also inheriting and amplifying pre-existing challenges in digital humanities and social sciences.
- · AI researchers in humanities
- · Digital historians
- · Computational linguists
- · Researchers solely relying on traditional qualitative methods
LLMs become standard tools for conceptual analysis across various academic disciplines.
New ethical and epistemological debates emerge regarding AI's interpretative authority and potential biases in historical and philosophical research.
The definition of 'understanding' and 'explanation' in academic inquiry is fundamentally reshaped by AI-driven conceptual frameworks.
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