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        <title>Team TIET at #SMM4H-HeaRD 2026: Fine-tuned Biomedical Transformers with Language-Balanced Sampling for Patient Metadata and Multilingual ADE Detection</title>
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        <namePart type="given">Divrose</namePart>
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            <namePart type="given">Guillermo</namePart>
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    <abstract>We present Team TIET’s systems for two shared tasks at #SMM4H-HeaRD 2026: Task 5 (detection of patient metadata in SARS-CoV-2 sequencing papers) and Task 1 (multilingual adverse drug event detection across six languages plus an unseen Farsi subset). For Task 5 we explore iterative LLM prompting followed by fine-tuning BiomedBERT-base with weighted cross-entropy loss and probability threshold optimization, achieving F1 = 0.760 on the official test set (above the competition mean of 0.729). For Task 1 we fine-tune XLM-RoBERTa-base with a combined language- and class-balanced sampling strategy and per-language threshold tuning, achieving macro F1 = 0.497 overall (0.608 excluding the unseen Farsi subset). We report empirical findings on BERT+LLM ensemble failure with bimodal probability distributions, the superiority of base over large model variants under limited data, and the importance of language-balanced gradient contribution in multilingual classification.</abstract>
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%0 Conference Proceedings
%T Team TIET at #SMM4H-HeaRD 2026: Fine-tuned Biomedical Transformers with Language-Balanced Sampling for Patient Metadata and Multilingual ADE Detection
%A Kaur, Divrose
%A Bedi, Jatin
%A Singh, Jasmeet
%Y Lopez-Garcia, Guillermo
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, United States
%@ 979-8-89176-432-3
%F kaur-etal-2026-team
%X We present Team TIET’s systems for two shared tasks at #SMM4H-HeaRD 2026: Task 5 (detection of patient metadata in SARS-CoV-2 sequencing papers) and Task 1 (multilingual adverse drug event detection across six languages plus an unseen Farsi subset). For Task 5 we explore iterative LLM prompting followed by fine-tuning BiomedBERT-base with weighted cross-entropy loss and probability threshold optimization, achieving F1 = 0.760 on the official test set (above the competition mean of 0.729). For Task 1 we fine-tune XLM-RoBERTa-base with a combined language- and class-balanced sampling strategy and per-language threshold tuning, achieving macro F1 = 0.497 overall (0.608 excluding the unseen Farsi subset). We report empirical findings on BERT+LLM ensemble failure with bimodal probability distributions, the superiority of base over large model variants under limited data, and the importance of language-balanced gradient contribution in multilingual classification.
%U https://aclanthology.org/2026.smm4h-1.44/
%P 268-271

Markdown (Informal)

[Team TIET at #SMM4H-HeaRD 2026: Fine-tuned Biomedical Transformers with Language-Balanced Sampling for Patient Metadata and Multilingual ADE Detection](https://aclanthology.org/2026.smm4h-1.44/) (Kaur et al., SMM4H 2026)

• Team TIET at #SMM4H-HeaRD 2026: Fine-tuned Biomedical Transformers with Language-Balanced Sampling for Patient Metadata and Multilingual ADE Detection (Kaur et al., SMM4H 2026)

ACL

• Divrose Kaur, Jatin Bedi, and Jasmeet Singh. 2026. Team TIET at #SMM4H-HeaRD 2026: Fine-tuned Biomedical Transformers with Language-Balanced Sampling for Patient Metadata and Multilingual ADE Detection. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 268–271, San Diego, United States. Association for Computational Linguistics.