Learning to Transfer Prompts for Text Generation
Bibliographic record
Abstract
Pretrained language models (PLMs) have made remarkable progress in text generation tasks via fine-tuning.While, it is challenging to fine-tune PLMs in a data-scarce situation.Therefore, it is non-trivial to develop a general and lightweight model that can adapt to various text generation tasks based on PLMs.To fulfill this purpose, the recent prompt-based learning offers a potential solution.In this paper, we improve this technique and propose a novel prompt-based method (PTG) for text generation in a transferable setting.First, PTG learns a set of source prompts for various source generation tasks and then transfers these prompts as target prompts to perform target generation tasks.To consider both task-and instance-level information, we design an adaptive attention mechanism to derive the target prompts.For each data instance, PTG learns a specific target prompt by attending to highly relevant source prompts.In extensive experiments, PTG yields competitive or better results than fine-tuning methods.We release our source prompts as an open resource, where users can add or reuse them to improve new text generation tasks for future research.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".