Language Resources From Prominent Born-Digital Humanities Texts are Still Needed in the Age of LLMs
Bibliographic record
Abstract
The digital humanities (DH) community fundamentally embraces the use of computerized tools for the study and creation of knowledge related to language, history, culture, and human values, in which natural language plays a prominent role.Many successful DH tools rely heavily on Natural Language Processing methods, and several efforts exist within the DH community to promote the use of newer and better tools.Nevertheless, most NLP research is driven by web corpora that are noticeably different from texts commonly found in DH artifacts, which tend to use richer language and refer to rarer entities.Thus, the near-human performance achieved by state-of-the-art NLP tools on web texts might not be achievable on DH texts.We introduce a dataset 1 carefully created by computer scientists and digital humanists intended to serve as a reference point for the development and evaluation of NLP tools.The dataset is a subset of a born-digital textbase resulting from a prominent and ongoing experiment in digital literary history, containing thousands of multi-sentence excerpts that are suited for information extraction tasks.We fully describe the dataset and show that its language is demonstrably different than the corpora normally used in training language resources in the NLP community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.111 | 0.035 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".