[Submitted on 7 Oct 2026]
Title:Grammar Concept Annotation at Scale: Deployed Fine-Tuned Small Language Models Outperform Prompted Frontier Models
View a PDF of the paper titled Grammar Concept Annotation at Scale: Deployed Fine-Tuned Small Language Models Outperform Prompted Frontier Models, by Marjan Celikik and 2 other authors
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Abstract: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 ($+13.2\%$).
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
ACM classes: I.2.7; I.2.6; K.3.1
Cite as: arXiv:2610.10827 [cs.CL]
(or arXiv:2610.10827v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.10827
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Marjan Celikik [view email] [v1] Wed, 7 Oct 2026 19:31:24 UTC (144 KB)
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