[
    {
        "id": "osp-15197",
        "type": "article-journal",
        "title": "Grammar Concept Annotation at Scale: Deployed Fine-Tuned Small Language Models Outperform Prompted Frontier Models",
        "author": [
            {
                "family": "Celikik",
                "given": "Marjan"
            },
            {
                "family": "Ramallo",
                "given": "Ana Peleteiro"
            },
            {
                "family": "Morales",
                "given": "Javier"
            }
        ],
        "URL": "https://omanscience.com/en/articles/grammar-concept-annotation-at-scale-deployed-fine-tuned-small-language-models-outperform-prompted-frontier-models",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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 ($+13.2\\%$)."
    }
]