Same File Prediction: A New Pretraining Objective forBERT-like Transformers
Bibliographic record
Abstract
Industrial applications can significantly benefit from leveraging pretrained Transformer-based language models, where their efficient exploitation of textual content provides a competitive edge to many processes. However, some specialized corpora and problems require additional handling to adequatly adapt to Transformer models. Our application involves one of these problems, because long-term dependencies in long longitudinal sequences of specialized texts require careful modeling. This paper proposes LongiBERT, a classification model that relies on a BERT-like transformer pretrained language model using our novel Same File Prediction training task. This pretraining objective captures repeated elements in a longitudinal text sequence. We evaluate this by studying the detection of costly insurance claims, a binary classification task using the private corpus of a major Canadian insurer. Our study indicates that our proposed model and pretraining objective yield more stable performance and outperform RoBERTa's robust MLM training approach for modeling long-term dependencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".