@inproceedings{caron-etal-2026-acss,
    title = "{ACSS}-{PSL} at {\#}{SMM}4{H}-{H}ea{RD} 2026: An {LLM}-Driven Autoresearch Loop for Opioid-Impact {NER}",
    author = "Caron, Olivier  and
      Ferreira, Bruno Chaves  and
      Benavent, Christophe",
    editor = "Lopez-Garcia, Guillermo  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the 11th Social Media Mining for Health Research and Applications ({SMM}4{H}-{H}ea{RD} 2026) Workshop and Shared Tasks",
    month = jul,
    year = "2026",
    address = "San Diego, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.smm4h-1.39/",
    pages = "240--245",
    ISBN = "979-8-89176-432-3",
    abstract = "We apply an LLM-driven autoresearch protocol to Task 7 of {\#}SMM4H-HeaRD 2026, which requires extracting ClinicalImpacts and SocialImpacts spans from Reddit posts about non-medical opioid use. A coding agent iteratively proposes a hypothesis, modifies the training configuration, and evaluates against the held-out validation set. Across 79 runs, only 9 improved strict F1, indicating a narrow viable search space on this small dataset (842 training examples). The submitted ensemble combines DeBERTa-large, MC Dropout blending, and a constrained multi-LLM consensus layer, reaching 0.46 strict and 0.52 relaxed F1 on test, though single-seed evaluation limits the reliability of run-level comparisons. The run log provides a reproducible case study of autonomous experimentation, including failure modes and guardrails for small-data NER."
}