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An archival perspective on pretraining data.

Meera A Desai1, Irene V Pasquetto2, Abigail Z Jacobs1

  • 1School of Information, University of Michigan, Ann Arbor, MI, USA.

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Summary
This summary is machine-generated.

Large language model (LLM) pretraining datasets are analyzed as informal archives. This archival lens reveals subjective value decisions and broader social impacts beyond model behavior.

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Area of Science:

  • Computer Science
  • Information Science
  • Archival Studies

Background:

  • The rapid advancement of large language models (LLMs) has led to the creation of massive pretraining datasets, often sourced from the web.
  • These datasets are crucial for LLM development but their creation and composition raise significant questions.

Purpose of the Study:

  • To analyze pretraining datasets using a framework from archival studies.
  • To identify impacts of pretraining data creation and use that extend beyond direct model performance.
  • To highlight the subjective value judgments inherent in dataset curation.

Main Methods:

  • Applying archival studies principles to conceptualize pretraining datasets as informal archives.
  • Examining the heterogeneous nature of these datasets and their role in mediating knowledge access.
  • Investigating the implications of inclusion and exclusion decisions in dataset construction.

Main Results:

  • Pretraining datasets function as informal archives, mediating access to diverse knowledge.
  • The process of curating these datasets involves subjective decisions about values and priorities.
  • Beyond shaping model behavior, dataset creation has significant social and ethical implications.

Conclusions:

  • An archival perspective offers a novel way to understand the complexities of LLM pretraining data.
  • Recognizing datasets as informal archives illuminates the subjective choices and value systems embedded within them.
  • This framework provides opportunities for researchers to address challenges in large-scale data creation and its societal impact.