Examines LLMs as research tools in HPSS, suggesting implications for interpretive practices.
Key Points
The aim is to explore the use of large language models in the history, philosophy, and sociology of science, along with their methodological implications.
Reviewed the architecture of large language models.
Analyzed the application of LLMs to historical data and scientific patterns.
Discussed adaptation strategies like fine-tuning and prompt-based learning.
Synthesized recent studies in HPSS and adjacent areas.
LLMs offer innovative ways to bridge detailed readings with large-scale analyses.
Identified that working with messy data and interpreting large patterns poses unique challenges.
Emphasized the need for LLM literacy and responsibility in their usage.
Highlighted the evolving role of LLMs in interpretative research methods.