Genetic-Based Lottery Ticket Pruning for Transformers in Sentiment Classification: Realized Through Lottery Sample Selection
Bibliographic record
Abstract
In the growing field of Natural Language Processing (NLP), transformers have become excessively large, pushing the boundaries of both training and inference compute. Given the size and widespread use of these models, there is now a strong emphasis on improving both training and inference efficiency. We propose an approach to reduce the computational requirements of transformers. We specifically tested this approach using BERT for sentiment classification. In particular, we reduced the number of attention heads in the model using the lottery ticket hypothesis and an adapted search strategy from a genetic-based lottery ticket pruning algorithm. This search process removes any need for full-sized model training and additionally reduces the training data by up to 95% through lottery sample selection. We achieve leading results in lossless head pruning with a 70% reduction in heads, and up to a 90% reduction with only a 1% F1 loss allocated. The search process was efficiently performed using 5% of training samples under random selection and was further shown to work with just 0.5% of samples by selecting a diverse set of sample embeddings. Inference time was also improved by up to 47.2%. We plan to generalize this work to Large Language Models (LLMs) and language generation tasks to improve both their training and inference requirements.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".