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            <namePart type="given">Guillermo</namePart>
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            <namePart type="given">Graciela</namePart>
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    <abstract>This paper describes the participation of team SMMTech in the SMM4H-HeaRD 2026 Shared Task 2: Detection of Insomnia in Clinical Notes. We present a comparative architectural study exploring the friction between extractive token-classification models and generative Large Language Models (LLMs) in clinical span extraction, on the MIMIC-III Clinical Database. During the validation phase we established baselines using encoder-only transformers such as BERT, ClinicalBERT, BigBird and Clinical BigBird. For the official test phase, we deployed a 4-bit quantized generative hybrid pipeline using Llama3-Med42-8B to evaluate its multi-hop reasoning capabilities. While the generative pipeline achieved an F1-score of 0.4783 on Subtask 1 (Classification), it struggled with exact span matching on Subtask 2.In this paper we present the mechanical limitations of zero-shot JSON extraction and the necessity of decoupling clinical reasoning from character-level span extraction.</abstract>
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%0 Conference Proceedings
%T SMMTech at #SMM4H-HeaRD 2026: Detection of Insomnia in Clinical Notes
%A Cristea, Emilia-Ioana
%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 cristea-2026-smmtech
%X This paper describes the participation of team SMMTech in the SMM4H-HeaRD 2026 Shared Task 2: Detection of Insomnia in Clinical Notes. We present a comparative architectural study exploring the friction between extractive token-classification models and generative Large Language Models (LLMs) in clinical span extraction, on the MIMIC-III Clinical Database. During the validation phase we established baselines using encoder-only transformers such as BERT, ClinicalBERT, BigBird and Clinical BigBird. For the official test phase, we deployed a 4-bit quantized generative hybrid pipeline using Llama3-Med42-8B to evaluate its multi-hop reasoning capabilities. While the generative pipeline achieved an F1-score of 0.4783 on Subtask 1 (Classification), it struggled with exact span matching on Subtask 2.In this paper we present the mechanical limitations of zero-shot JSON extraction and the necessity of decoupling clinical reasoning from character-level span extraction.
%U https://aclanthology.org/2026.smm4h-1.15/
%P 88-92

Markdown (Informal)

[SMMTech at #SMM4H-HeaRD 2026: Detection of Insomnia in Clinical Notes](https://aclanthology.org/2026.smm4h-1.15/) (Cristea, SMM4H 2026)

• SMMTech at #SMM4H-HeaRD 2026: Detection of Insomnia in Clinical Notes (Cristea, SMM4H 2026)

ACL

• Emilia-Ioana Cristea. 2026. SMMTech at #SMM4H-HeaRD 2026: Detection of Insomnia in Clinical Notes. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 88–92, San Diego, United States. Association for Computational Linguistics.