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        <title>Limics at #SMM4H-HeaRD 2026: Uncertainty-Driven Prediction for ADE Detection in Social Media</title>
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            <title>Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks</title>
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        <name type="personal">
            <namePart type="given">Guillermo</namePart>
            <namePart type="family">Lopez-Garcia</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Graciela</namePart>
            <namePart type="family">Gonzalez-Hernandez</namePart>
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    <abstract>This paper describes our system for the SMM4H-HeaRD 2026 Task 1: Detection of Adverse Drug Events in Multilingual and Multi-platform Social Media Posts. We developed a two-stage pipeline combining a fine-tuned XLM-RoBERTa-large encoder-only model with a large language model for final decision on ambiguous cases. To handle complex linguistic boundaries, we explore explicitly training the encoder to treat ambiguity as a discrete third label to delegate those cases to the generative model. Although introducing the third label was associated with lower performance than relying on a binary model, when using the encoder as a preliminary filter for classifying 78.62% of posts as negatives, we achieved a global F₁ score of 0.614 (+0.034 over task median).</abstract>
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%0 Conference Proceedings
%T Limics at #SMM4H-HeaRD 2026: Uncertainty-Driven Prediction for ADE Detection in Social Media
%A Allam, Nour
%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 allam-2026-limics
%X This paper describes our system for the SMM4H-HeaRD 2026 Task 1: Detection of Adverse Drug Events in Multilingual and Multi-platform Social Media Posts. We developed a two-stage pipeline combining a fine-tuned XLM-RoBERTa-large encoder-only model with a large language model for final decision on ambiguous cases. To handle complex linguistic boundaries, we explore explicitly training the encoder to treat ambiguity as a discrete third label to delegate those cases to the generative model. Although introducing the third label was associated with lower performance than relying on a binary model, when using the encoder as a preliminary filter for classifying 78.62% of posts as negatives, we achieved a global F₁ score of 0.614 (+0.034 over task median).
%U https://aclanthology.org/2026.smm4h-1.37/
%P 230-236

Markdown (Informal)

[Limics at #SMM4H-HeaRD 2026: Uncertainty-Driven Prediction for ADE Detection in Social Media](https://aclanthology.org/2026.smm4h-1.37/) (Allam, SMM4H 2026)

• Limics at #SMM4H-HeaRD 2026: Uncertainty-Driven Prediction for ADE Detection in Social Media (Allam, SMM4H 2026)

ACL

• Nour Allam. 2026. Limics at #SMM4H-HeaRD 2026: Uncertainty-Driven Prediction for ADE Detection in Social Media. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 230–236, San Diego, United States. Association for Computational Linguistics.