Bigger is not always better: The importance of human-scale language modeling for psycholinguistics
Bibliographic record
Abstract
Neural network language models can learn a surprising amount about language by predicting upcoming words in a corpus. Recent language technologies work has demonstrated that large performance improvements can arise from simply increasing ("scaling") the size of the data sets they are trained on (and, correspondingly, the number of parameters in those models); accordingly, many contemporary systems are trained on trillions of words. While largely beneficial to performance on language applications, scaling has several downsides for both computational psycholinguistics and natural language processing research. We discuss the scientific challenges presented by scaling, as well as the benefits that would result from human-scale language modeling research. In the second half of this paper, we report on takeaways from two efforts to bring about human-scale language model pretraining. First, we report on the first iteration of the BabyLM Challenge, a shared task organized by the authors that asked participants to train a language model on 100 million words or less. Second, we present experiments to answer open questions from the findings of the BabyLM Challenge: namely, are a significant amount of computational resources required to achieve high performance, even at such small scales? We find that high performance can be achieved at small data scales and with typical academic-scale computational resources.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".