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            <namePart type="given">Guillermo</namePart>
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            <namePart type="given">Graciela</namePart>
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    <abstract>Identifying sentences in COVID-19 literature that report patient metadata is an important step in genomic epidemiology, currently requiring costly manual curation. We compare fine-tuned encoder-only models (BERT, BioLinkBERT) and autoregressive LLMs (Llama, Gemma, GPT-OSS) under prompting and fine-tuning regimes, using Focal Loss and undersampling to address severe class imbalance. Encoder-only models substantially outperform autoregressive models: BioLinkBERT-base with Focal Loss achieves macro F1 of 0.76, versus 0.54 for the best fine-tuned autoregressive model.</abstract>
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%0 Conference Proceedings
%T No_gmail at #SMM4H-HeaRD 2026: Detecting Patient Metadata in COVID-19 Scientific Literature: A Comparative Study of Encoder-Only and Autoregressive Language Models
%A Anastasia, Stefanescu
%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 anastasia-2026-gmail
%X Identifying sentences in COVID-19 literature that report patient metadata is an important step in genomic epidemiology, currently requiring costly manual curation. We compare fine-tuned encoder-only models (BERT, BioLinkBERT) and autoregressive LLMs (Llama, Gemma, GPT-OSS) under prompting and fine-tuning regimes, using Focal Loss and undersampling to address severe class imbalance. Encoder-only models substantially outperform autoregressive models: BioLinkBERT-base with Focal Loss achieves macro F1 of 0.76, versus 0.54 for the best fine-tuned autoregressive model.
%U https://aclanthology.org/2026.smm4h-1.46/
%P 280-285

Markdown (Informal)

[No_gmail at #SMM4H-HeaRD 2026: Detecting Patient Metadata in COVID-19 Scientific Literature: A Comparative Study of Encoder-Only and Autoregressive Language Models](https://aclanthology.org/2026.smm4h-1.46/) (Anastasia, SMM4H 2026)

• No_gmail at #SMM4H-HeaRD 2026: Detecting Patient Metadata in COVID-19 Scientific Literature: A Comparative Study of Encoder-Only and Autoregressive Language Models (Anastasia, SMM4H 2026)

ACL

• Stefanescu Anastasia. 2026. No_gmail at #SMM4H-HeaRD 2026: Detecting Patient Metadata in COVID-19 Scientific Literature: A Comparative Study of Encoder-Only and Autoregressive Language Models. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 280–285, San Diego, United States. Association for Computational Linguistics.