United States

Detecting insomnia from clinical narratives re-
quires both accurate classification and clini-
cally grounded reasoning with interpretable evi-
dence. We present our systems for the SMM4H-
HeaRD 2026 shared task, which leverages
MIMIC-III notes annotated with rule-based in-
somnia criteria and supporting evidence spans.
We explore two complementary approaches:
parameter-efficient fine-tuning of lightweight
models using QLoRA and LoRA, and few-shot
prompting of large language models for joint
reasoning and evidence extraction. Our best
system achieves an F1-score of 0.7333 on bi-
nary classification and a micro-F1 of 0.6535 on
multi-label rule prediction, with up to 0.5192
partial-match F1 for evidence extraction. Re-
sults show that lightweight fine-tuned models
can outperform larger models in classification,
while larger models demonstrate stronger rea-
soning but struggle with precise span localiza-
tion, highlighting a key gap in clinically inter-
pretable NLP systems.

ACL 2026

A3S@C-DAC at #SMM4H-HeaRD 2026: Reasoning Meets Evidence:
LLMs for Interpretable Insomnia Detection with Evidence Extraction in
Clinical Notes

insomnia detection

clinical reasoning

qlora

lora

evidence extraction

parameter-efficient fine-tuning

mimic-iii

large language models

explainable ai

few-shot learning

clinical nlp

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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workshop paper

This paper describes the Gladiators system for
Task 1 of the SMM4H 2026 shared task on bi-
nary classification of adverse drug event (ADE)
mentions in multilingual social media posts.
Our system fine-tunes three XLM-RoBERTa-
large models with different random seeds us-
ing focal loss (α=0.75, γ=2.0) and 3×pos-
itive oversampling, then averages their pre-
dicted probabilities and applies per-language
threshold optimization. On the development
set, our ensemble achieves a pooled binary F1
of 0.7505. On the official test set—which in-
troduced surprise Farsi comprising 35.5% of
samples—our system achieves F1 = 0.6039,
above the competition mean (0.5465) and me-
dian (0.5798). We evaluated eleven approaches,
document key negative results including catas-
trophic translation augmentation failures, and
analyze precision–recall trade-offs under se-
vere class imbalance. Post-evaluation, a six-
model cross-regime ensemble improved dev F1
to 0.7585.

Gladiators at #SMM4H–HeaRD 2026: Multi-Seed XLM-RoBERTa
Ensemble with Focal Loss
and Per-Language Threshold Optimization for Multilingual Adverse Drug
Event Detection

This paper describes the participation of the
LSI_UNED team in the firt sub-task of Mul-
tiClinAI at the #SMM4H-HeaRD 2026 Work-
shop, which focuses on multilingual clinical
named entity recognition in seven languages.
The task requires identifying mentions of dis-
eases, procedures, and symptoms in clinical
case reports. We propose a set of systems
based on the W2NER architecture, with a sepa-
rate model trained for each language and entity
type. For Spanish, we use a RoBERTa-based
model with data augmentation from additional
NER resources, while English and Italian sys-
tems are based on different biomedical BERT
variants. Results show consistent performance
across languages, with the best overall results
obtained for Spanish. Data augmentation im-
proves recall and F1, while English and Italian
models achieve competitive but slightly lower
scores. Symptom recognition remains the most
challenging entity type across all languages.

LSI_UNED at #SMM4H–HeaRD 2026: Grid-Based Biomedical Named
Entity Recognition Across Languages and Entity Types

We describe the LLATMU systems submitted
to the #SMM4H–HeaRD 2026 shared tasks,
covering two related clinical text structuring
problems: dialogue-to-SOAP note generation
(Task 4) and TNM staging classification from
pathology reports (Task 6). Although the two
tasks differ in modeling paradigm—text gen-
eration versus supervised classification—both
require transforming unstructured clinical nar-
ratives into structured representations.
For Task 4, we instruction-tuned LLMs with
parameter-efficient adaptation and submitted
a QLoRA-based Ministral-3B system, achiev-
ing an official blind test average score of 0.53
and outperforming the task-wide mean and me-
dian. For Task 6, we formulate TNM predic-
tion as a three-head classification problem us-
ing BioClinical-ModernBERT-large with long-
context encoding, class-weighted loss, and
normalized partial-label training. The model
achieves a validation average macro-F1 of
0.9196 and continues to outperform the offi-
cial baseline on the more challenging tie-break
test set.
Across both tasks, our results suggest that ro-
bust data handling, stable fine-tuning, and task-
appropriate supervision are important for prac-
tical clinical NLP under constrained and imper-
fect shared-task settings.

LLATMU at #SMM4H–HeaRD 2026: Clinical Text Structuring with QLoRA-based Generation and Partial-Label TNM Classification

We present a lightweight, deterministic post-
processing approach for clinical text classifi-
cation based on entanglement between clini-
cally meaningful concepts. Our system was
developed for the SMM4H 2026 shared task on
insomnia detection and related information ex-
traction from clinical notes. For Subtask 1, we
introduce an entanglement-based rescue layer
that models dependencies between sleep dis-
turbance, daytime impairment, and evidence
of sleep-targeted medications. Applied as a
false-negative correction on top of an LLM
baseline, this approach improves recall while
preserving precision. In the official test set, the
rescue layer increases F1 by 25% without de-
grading precision (1.00). Local experiments
show larger gains on weaker runs, suggesting a
stabilizing effect on variable LLM output. For
Subtask 2, we implement an LLM-based sys-
tem for rule-based evidence and span extraction.
Results highlight the effectiveness of modeling
clinically grounded dependencies and suggest
directions to improve evidence extraction and
span matching.

In2Lab-TNT at #SMM4H-HeaRD 2026: An Application of QTT’sTerminological Entanglement to Leverage Insomnia Detection in ClinicalNotes

We describe our systems for #SMM4H-HeaRD
2026 Task 6, which requires predicting the T, N,
and M components of pathological TNM stage
from TCGA pathology reports. We explored
both discriminative long-context encoders and
generative biomedical LLMs. For the first
test set, our BioClinical-ModernBERT-large
ensemble achieved 0.993 micro-F1 and 0.915
macro-F1, improving over the BB-TEN base-
line scoring-log result of 0.947 micro-F1 and
0.780 macro-F1. For the harder second test
set, our OpenBioLLM-8B LoRA extractor im-
proved component macro-F1 over the organizer
baseline from 0.454 to 0.626 for T, from 0.591
to 0.758 for N, and from 0.554 to 1.000 for
M. These results suggest that long-context en-
coders are strong for explicit T and N evidence,
while constrained generative LLM extraction
can be effective for harder reports. The main
remaining weakness is rare-class T4 recogni-
tion.

GoBlueInformatics at #SMM4H-HeaRD 2026: Long-Context Encoders and
Generative Biomedical LLMs for Pathological TNM Stage Prediction*

This paper addresses the MultiClinAI chal-
lenge, subtask MultiClinNER, which focuses
on clinical Named Entity Recognition (NER)
across seven languages: Czech, Dutch, En-
glish, Italian, Romanian, Spanish, and Swedish.
The main goal of MultiClinNER is to identify
and extract clinical terms specifically related
to diseases, procedures, and symptoms from
discharge summaries. The paper explores a va-
riety of state-of-the-art methods, both monolin-
gual and multilingual, ranging from pretrained,
zero-shot, domain-adapted transformers to fine-
tuned transformer models, and demonstrates
the benefits of ensemble modeling. Data aug-
mentation through external resources signifi-
cantly enhanced the models’ ability to recog-
nize clinical entities. Both monolingual and
multilingual approaches showed complemen-
tary strengths depending on the language and
entity type. The average F1 score achieved
across the best models for each language and
category is 0.6502.

Enigma at #SMM4H–HeaRD 2026: Leveraging Multilingual Pre-trained
Models for Clinical Named Entity Recognition

In this work, we address the detection of drug
abuse repercussions in Reddit posts, as part of
SMM4H-HeaRD Task 7: Extraction of Social
and Clinical Impacts of Substance Use from
Social Media Posts. We evaluate multiple ap-
proaches, including fine-tuning and zero-shot
inference, across several deep learning archi-
tectures. Our best result is obtained using an
adapter-based fine-tuning approach on the De-
BERTaV3 model. In addition, we explore text-
based evolutionary optimization for Gemma
4 workflows and show that, on this task, they
achieve competitive performance with the su-
pervised DeBERTaV3 setup.

RACAI at #SMM4H-HeaRD: Named Entity Recognition for Detecting the Impacts of Drug Abuse in Social Media Posts: Zero-Shot and Fine-Tuning Approaches

This paper presents our submission to the Mul-
tiClinAI Shared Task (Gallego-Donoso et al.,
2026) on cross-lingual clinical entity annota-
tion projection from Spanish to English. Our
system transfers expert annotations for Dis-
eases, Symptoms and Procedures entities. The
approach integrates three core components:
adaptive candidate window generation, an XG-
Boost classifier leveraging surface and seman-
tic features, and an LLM-based post-processing
stage to resolve complex misalignments. Our
highest-performing run ranked 3rd on the offi-
cial leaderboard, achieving strict F1 scores of
0.737, 0.549, and 0.538 for Diseases, Proce-
dures, and Symptoms, respectively. These re-
sults show that combining supervised candidate
scoring with targeted LLM refinement provides
a robust strategy for clinical entity projection.

ICB-UMA at #SMM4H–HeaRD 2026: Hybrid Clinical Entity Projection for MultiClinAI: Adaptive Candidate Windows, XGBoost, and LLM Refinement

This paper presents our submission to Multi-
ClinAI’s NER subtask for #SMM4H-HeaRD
2026. We focus on the questions 1) which
Language Model represents the clinical notes
best and 2) which annotations can help train-
ing these models. To get answers for these
questions, we follow a token-based classifica-
tion approach with pretrained encoder language
models, where we compare models that were
pretrained on generic data against medical data,
and on a single language, Dutch, against many
languages. In addition, we present two data-
augmented systems: one with data from the
other languages of the workshop for multilin-
gual training, and one with synthetic annota-
tions1
.

LotusOrchid at #SMM4H–HeaRD 2026: Fitting pretrained encoders for
Dutch medical data

This paper presents our approach to Task 5 of
the #SMM4H-HeaRD 2026 Workshop, which
focuses on detecting patient metadata in SARS-
CoV-2 sequencing articles as a binary classi-
fication task. We explore both encoder-based
and large language model (LLM) approaches,
using BioM-BERT as a baseline and Mistral-
Nemo as the LLM. To improve performance,
we propose a paraphrase-based data augmenta-
tion pipeline using Qwen3, where paraphrased
training and validation instances are added for
fine-tuning. For the LLM, we perform prompt
refinement and error analysis, while for the
encoder-based model, we reformulate the task
as a hypothesis-conditioned classification task
inspired by Natural Language Inference (NLI).
Our methods improve both models: Mistral-
Nemo increases from 0.423 to 0.750 F1, and
BioM-BERT from 0.801 to 0.821 on the valida-
tion set. Although Mistral-Nemo does not sur-
pass BioM-BERT, our best BioM-BERT model
achieves an F1-score of 0.786 on the test set,
outperforming the mean and median of com-
peting systems. To support reproducibility, we
release our best-performing model on Hugging
Face.

Downloads

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