Residual Prompt Tuning: improving prompt tuning with residual reparameterization
Bibliographic record
Abstract
Prompt tuning is one of the successful approaches for parameter-efficient tuning of pretrained language models.Despite being arguably the most parameter-efficient (tuned soft prompts constitute < 0.1% of total parameters), it typically performs worse than other efficient tuning methods and is quite sensitive to hyper-parameters.In this work, we introduce RESIDUAL PROMPT TUNING -a simple and efficient method that significantly improves the performance and stability of prompt tuning.We propose to reparameterize soft prompt embeddings using a shallow network with a residual connection.Our experiments show that RESIDUAL PROMPT TUNING significantly outperforms prompt tuning on SuperGLUE benchmark across T5-Large, T5-Base and BERT-Base models.Notably, our method reaches +7 points improvement over prompt tuning with T5-Base and allows to reduce the prompt length by ×10 without hurting performance.In addition, we show that our approach is robust to the choice of learning rate and prompt initialization, and is effective in few-shot settings.1
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 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 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".