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        <title>IITPatna_ADE at #SMM4H-HeaRD 2026: Multilingual Adverse Drug Event Detection with LoRA-XLM-RoBERTa, Cross-Fold Ensembles, and Post-hoc Calibration</title>
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        <namePart type="given">Manish</namePart>
        <namePart type="family">Singh</namePart>
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    <abstract>We describe our submission to Task 1 of #SMM4H-HeaRD 2026: multilingual binary classification of adverse drug event (ADE) mentions in social media. Our system fine-tunes xlm-roberta-large with LoRA adapters and learned language embeddings, using two-stage training (CADEC translated domain adaptation, then five-fold cross-validation on the official training set). We ensemble the five fold checkpoints by mean logits, apply temperature scaling on the development set, and tune decision thresholds to maximize the official metric. On development, the final ensemble reaches macro-F₁ 0.788 with a global threshold and 0.796 with per-language thresholds; our best official test submission achieves macro-F₁ 0.616 (ID 678990).</abstract>
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%0 Conference Proceedings
%T IITPatna_ADE at #SMM4H-HeaRD 2026: Multilingual Adverse Drug Event Detection with LoRA-XLM-RoBERTa, Cross-Fold Ensembles, and Post-hoc Calibration
%A Jamil, Sofia
%A Singh, Manish
%A Dharpure, Harshal
%A Saha, Sriparna
%A Misra, Rajiv
%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 jamil-etal-2026-iitpatna
%X We describe our submission to Task 1 of #SMM4H-HeaRD 2026: multilingual binary classification of adverse drug event (ADE) mentions in social media. Our system fine-tunes xlm-roberta-large with LoRA adapters and learned language embeddings, using two-stage training (CADEC translated domain adaptation, then five-fold cross-validation on the official training set). We ensemble the five fold checkpoints by mean logits, apply temperature scaling on the development set, and tune decision thresholds to maximize the official metric. On development, the final ensemble reaches macro-F₁ 0.788 with a global threshold and 0.796 with per-language thresholds; our best official test submission achieves macro-F₁ 0.616 (ID 678990).
%U https://aclanthology.org/2026.smm4h-1.29/
%P 177-181

Markdown (Informal)

[IITPatna_ADE at #SMM4H-HeaRD 2026: Multilingual Adverse Drug Event Detection with LoRA-XLM-RoBERTa, Cross-Fold Ensembles, and Post-hoc Calibration](https://aclanthology.org/2026.smm4h-1.29/) (Jamil et al., SMM4H 2026)

• IITPatna_ADE at #SMM4H-HeaRD 2026: Multilingual Adverse Drug Event Detection with LoRA-XLM-RoBERTa, Cross-Fold Ensembles, and Post-hoc Calibration (Jamil et al., SMM4H 2026)

ACL

• Sofia Jamil, Manish Singh, Harshal Dharpure, Sriparna Saha, and Rajiv Misra. 2026. IITPatna_ADE at #SMM4H-HeaRD 2026: Multilingual Adverse Drug Event Detection with LoRA-XLM-RoBERTa, Cross-Fold Ensembles, and Post-hoc Calibration. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 177–181, San Diego, United States. Association for Computational Linguistics.