ReviewPatterns (New York, N.Y.)2024
An archival perspective on pretraining data.
Review in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Computational hermeneutics: evaluating generative AI as a cultural technology.Frontiers in artificial intelligence · 2026Article
- No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2025Article
- Model interpretability enhances domain generalization in the case of textual complexity modeling.Patterns (New York, N.Y.) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alongside an explosion in research and development related to large language models, there has been a concomitant rise in the creation of pretraining datasets-massive collections of text, typically scraped from the web. Drawing on the field of archival studies, we analyze pretraining datasets as informal archives-heterogeneous collections of diverse material that mediate access to knowledge. We use this framework to identify impacts of pretraining data creation and use beyond directly shaping model behavior and reveal how choices about what is included in pretraining data necessarily involve subjective decisions about values. In doing so, the archival perspective helps us identify opportunities for researchers who study the social impacts of technology to contribute to confronting the challenges and trade-offs that arise in creating pretraining datasets at this scale.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.