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        <name type="personal">
            <namePart type="given">Guillermo</namePart>
            <namePart type="family">Lopez-Garcia</namePart>
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            <namePart type="given">Graciela</namePart>
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    <abstract>This paper presents our systems for the SMM4H 2026 shared task on flu-related tweetclassification across two subtasks: flu vaccination status and flu test outcome classification. For each subtask, we evaluate two approaches: fine-tuning BERTweet-large with atemporal-aware architecture, cross-validation ensembling, and regularization techniques, anda GPT-4o few-shot prompting system with similarity-based dynamic example retrieval,chain-of-thought reasoning and contrastive label ranking. Fine-tuning proves superior for theflu vaccination subtask (micro-F1: 87.90%), where sufficient and relatively balanced training datais available, while few-shot prompting performs better for the flu test subtask (micro-F1: 95.74%), where limited and heavily imbalanced training data renders fine-tuning less effective.</abstract>
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%0 Conference Proceedings
%T BioNLP at #SMM4H-HeaRD 2026 Task 3 Estimating Flu Vaccine Effectiveness: A Temporal-Aware Fine-Tuning and Similarity-Based Few-Shot Prompting Approach
%A Patularu, Irina
%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 patularu-2026-bionlp
%X This paper presents our systems for the SMM4H 2026 shared task on flu-related tweetclassification across two subtasks: flu vaccination status and flu test outcome classification. For each subtask, we evaluate two approaches: fine-tuning BERTweet-large with atemporal-aware architecture, cross-validation ensembling, and regularization techniques, anda GPT-4o few-shot prompting system with similarity-based dynamic example retrieval,chain-of-thought reasoning and contrastive label ranking. Fine-tuning proves superior for theflu vaccination subtask (micro-F1: 87.90%), where sufficient and relatively balanced training datais available, while few-shot prompting performs better for the flu test subtask (micro-F1: 95.74%), where limited and heavily imbalanced training data renders fine-tuning less effective.
%U https://aclanthology.org/2026.smm4h-1.41/
%P 252-259

Markdown (Informal)

[BioNLP at #SMM4H-HeaRD 2026 Task 3 Estimating Flu Vaccine Effectiveness: A Temporal-Aware Fine-Tuning and Similarity-Based Few-Shot Prompting Approach](https://aclanthology.org/2026.smm4h-1.41/) (Patularu, SMM4H 2026)

• BioNLP at #SMM4H-HeaRD 2026 Task 3 Estimating Flu Vaccine Effectiveness: A Temporal-Aware Fine-Tuning and Similarity-Based Few-Shot Prompting Approach (Patularu, SMM4H 2026)

ACL

• Irina Patularu. 2026. BioNLP at #SMM4H-HeaRD 2026 Task 3 Estimating Flu Vaccine Effectiveness: A Temporal-Aware Fine-Tuning and Similarity-Based Few-Shot Prompting Approach. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 252–259, San Diego, United States. Association for Computational Linguistics.