الملخص
Corrective feedback is among the best-evidenced drivers of second-language acquisition, yet corrections delivered during lessons rarely accumulate into an actionable view of grammar mastery. Prompted frontier models can provide such a view from learner--tutor lesson transcripts, but they are costly at scale. We close this gap by fine-tuning Qwen3.5 small language models (SLMs) on filtered and rebalanced teacher-generated supervision, then deploying an efficient 0.8B model in an end-to-end grammar mastery tracker for all English learners on our platform. Internalizing the annotation contract into adapter weights enables pairing the 0.8B model with a compact matched prompt rather than verbose instructions. On two human-curated benchmarks, both the deployed 0.8B model and a 4B reference comparator outperform prompted GPT-5.4 and GPT-5.6 Sol in precision and recall under nested matching criteria of increasing strictness: concept, evidence span, and correctness. The deployed 0.8B SLM reduces serving cost by approximately 16$\times$. A feature-level online experiment shows significant gains in learner engagement ($+15.8\%$) and key business metrics, including scheduled hours ($+2.1\%$) and GMV from new lessons ($+13.2\%$).
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Celikik, M., Ramallo, A. P., & Morales, J. (2026). Grammar Concept Annotation at Scale: Deployed Fine-Tuned Small Language Models Outperform Prompted Frontier Models. https://omanscience.com/ar/articles/grammar-concept-annotation-at-scale-deployed-fine-tuned-small-language-models-outperform-prompted-frontier-models
MLA 9
Celikik, Marjan, et al. "Grammar Concept Annotation at Scale: Deployed Fine-Tuned Small Language Models Outperform Prompted Frontier Models." https://omanscience.com/ar/articles/grammar-concept-annotation-at-scale-deployed-fine-tuned-small-language-models-outperform-prompted-frontier-models.
شيكاغو (المؤلف–التاريخ)
Celikik, Marjan, Ana Peleteiro Ramallo, and Javier Morales. 2026. "Grammar Concept Annotation at Scale: Deployed Fine-Tuned Small Language Models Outperform Prompted Frontier Models." https://omanscience.com/ar/articles/grammar-concept-annotation-at-scale-deployed-fine-tuned-small-language-models-outperform-prompted-frontier-models.
هارفارد
Celikik, M., Ramallo, A. P. and Morales, J. (2026) 'Grammar Concept Annotation at Scale: Deployed Fine-Tuned Small Language Models Outperform Prompted Frontier Models', Available at: https://omanscience.com/ar/articles/grammar-concept-annotation-at-scale-deployed-fine-tuned-small-language-models-outperform-prompted-frontier-models.
فانكوفر
Celikik M, Ramallo AP, Morales J. Grammar Concept Annotation at Scale: Deployed Fine-Tuned Small Language Models Outperform Prompted Frontier Models. https://omanscience.com/ar/articles/grammar-concept-annotation-at-scale-deployed-fine-tuned-small-language-models-outperform-prompted-frontier-models
IEEE
M. Celikik, A. P. Ramallo, and J. Morales, "Grammar Concept Annotation at Scale: Deployed Fine-Tuned Small Language Models Outperform Prompted Frontier Models," https://omanscience.com/ar/articles/grammar-concept-annotation-at-scale-deployed-fine-tuned-small-language-models-outperform-prompted-frontier-models.