Rethinking Full Finetuning from Pretraining Checkpoints in Active Learning for African Languages
Bibliographic record
Abstract
Active learning (AL) aims to reduce annotation effort by iteratively selecting the most informative samples for labeling.The dominant strategy in AL involves fully finetuning the model on all acquired data after each round, which is computationally expensive in multilingual and low-resource settings.This paper investigates continual finetuning (CF), an alternative update strategy where the model is updated only on newly acquired samples at each round.We evaluate CF against full finetuning (FA) across 28 African languages using MasakhaNEWS and SIB-200.Our analysis reveals three key findings.First, CF matches or outperforms FA for languages included in the model's pretraining, achieving up to 35% reductions in GPU memory, FLOPs, and training time.Second, CF performs comparably even for languages not seen during pretraining when they are typologically similar to those that were.Third, CF's effectiveness depends critically on uncertainty-based acquisition; without it, performance deteriorates significantly.While FA remains preferable for some low-resource languages, the overall results establish CF as a robust, cost-efficient alternative for active learning in multilingual NLP.These findings motivate the development of hybrid AL strategies that adapt fine-tuning behavior based on pretraining coverage, language typology, and acquisition dynamics.Our code is available here.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".