Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning
Bibliographic record
Abstract
Unified Sequence Labeling articulates different sequence labeling tasks such as Named Entity Recognition, Relation Extraction, Semantic Role Labeling, etc. in a generalized sequence-to-sequence format.Unfortunately, this requires formatting different tasks into specialized augmented formats which are unfamiliar to the base pretrained language model (PLMs).This necessitates model fine-tuning and significantly bounds its usefulness in datalimited settings where fine-tuning large models cannot properly generalize to the target format.To address this challenge and leverage PLM knowledge effectively, we propose FISH-DIP, a sample-aware dynamic sparse finetuning strategy.It selectively finetunes a fraction of parameters informed by highly regressing examples during the fine-tuning process.By leveraging the dynamism of sparsity, our approach mitigates the impact of well-learned samples and prioritizes underperforming instances for improvement in generalization.Across five tasks of sequence labeling, we demonstrate that FISH-DIP can smoothly optimize the model in low-resource settings, offering up to 40% performance improvements over full fine-tuning depending on target evaluation settings.Also, compared to in-context learning and other parameter-efficient fine-tuning (PEFT) approaches, FISH-DIP performs comparably or better, notably in extreme lowresource settings.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".