LLMs in alliance with Edit-based models: advancing In-Context Learning for Grammatical Error Correction by Specific Example Selection
Bibliographic record
Abstract
We show that fewshot Grammatical Error Correction might be improved by using an encoderbased sequence labeling model, such as GEC-TOR, to select similar examples.We demonstrate this on three Russian GEC corpora and English BEA corpus.The effect is the most significant for the new LORuGEC corpus and reaches up to 5-10% F0.5-score depending on the model.The corpus is released in our paper and contains 348 train and 612 test examples.The corpus is designed for diagnostic purposes and is also equipped with writing rules' annotations.These annotations allow to further improve fewshot error correction by contrastive tuning of GECTOR-like encoder on rule classification task.This holds for a broad class of large language models.The best results are obtained with 5-shot YandexGPT-5 Pro model, achieving F0.5-score of 83%.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".