Multi-Mask Label Mapping for Prompt-Based Learning
Bibliographic record
Abstract
Prompt-based Learning has shown significant success in few-shot classification. The mainstream approach is to concatenate a template for the input text to transform the classification task into a cloze-type task where label mapping plays an important role in finding the ground-truth labels. While current label mapping methods only use the contexts in one single input, it could be crucial if wrong information is contained in the text. Specifically, it is proved in recent work that even the large language models like BERT/RoBERTa make classification decisions heavily dependent on a specific keyword regardless of the task or the context. Such a word is referred to as a lexical cue and if a misleading lexical cue is included in the instance it will lead the model to make a wrong prediction. We propose a multi-mask prompt-based approach with Multi-Mask Label Mapping (MMLM) to reduce the impact of misleading lexical cues by allowing the model to exploit multiple lexical cues. To satisfy the conditions of few-shot learning, an instance augmentation approach for the cloze-type model is proposed and the misleading cues are gradually excluded through training. We demonstrate the effectiveness of MMLM by both theoretical analysis and empirical studies, and show that MMLM outperforms other existing label mapping approaches.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.004 | 0.003 |
| Research integrity | 0.003 | 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".