United States

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.

ACL 2026

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

tnm staging classification

soap note generation

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.

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workshop paper

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.

PEI at #SMM4H-HeaRD 2026: Enhancing Patient Metadata Detection via
Hypothesis-Conditioned Classification and Paraphrase-Based Data
Augmentation

We present a system for multilingual clini-
cal named entity recognition (NER) submitted
to the MultiClinNER subtask of MultiClinAI
2026, covering all seven languages and three
entity classes (disease, symptom, procedure).
Our approach trains one binary token classifier
ensemble per entity class using cross-lingual
fine-tuning of XLM-RoBERTa-large, with all
languages handled jointly. We apply character-
level ensembling over six models (two encoder
variants × three cross-validation folds). This
ensembling method provides more granular
probability estimates than single-model clas-
sifiers, allowing for more flexible precision-
recall trade-off tuning. The system achieves
character-level F1 scores of 0.70–0.88 on the
official test set.

Discovery@FI at #SMM4H–HeaRD 2026:
Ensemble Character Classifier for Multilingual Clinical NER

In this paper, we describe systems for two
#SMM4H-HeaRD 2026 shared tasks. Task 6
asks for per-axis TNM cancer staging from
free-text TCGA pathology reports under se-
vere label imbalance and long-document con-
straints. We fine-tune GatorTron-base sep-
arately on each axis using Focal loss with
class weights and a pooled [CLS]–mean rep-
resentation, reaching macro F1 of 0.700 (T),
0.774 (N), and 0.640 (M) on test set 2 against
a baseline of 0.454, 0.591, and 0.554 re-
spectively. Task 7 asks for span-level detec-
tion of opioid-related ClinicalImpacts and So-
cialImpacts in first-person Reddit posts. We
combine DeBERTa-large and PubMedBERT
(two seeds each) in a uniform-weight ensemble
with boundary-aware loss, entity-replacement
augmentation, and a first-person post filter,
achieving strict F1 of 0.51 and relaxed F1 of
0.60, above both the task mean (0.46 / 0.55) and
median (0.48 / 0.58).

CUET_DiagNLP at #SMM4H-HeaRD 2026: Per-Axis TNM Staging from
Pathology Reports and Opioid Impact Span Detection from Social Media

We present an overview of the MultiClinAI shared task, which focuses on multilingual clinical entity extraction and automatic corpus generation through annotation projection. It addresses two key challenges in clinical natural language processing (NLP): (i) developing comparable multilingual named entity recognition (NER) systems and (ii) automatically constructing multilingual clinical corpora through annotation projection. The MultiClinAI task provides a unified benchmark for evaluating multilingual and cross-lingual clinical NLP approaches that cover diseases, symptoms, and procedures in Spanish, English, Dutch, Italian, Romanian, Swedish, and Czech. A total of 21 teams from 13 countries participated, submitting 531 runs across the different subtasks. The top runs obtained very competitive results across several languages and entity types. The results highlight both the challenges and opportunities of multilingual clinical information extraction. All resources, including a corpus of over 738,201 manually revised entity mentions across seven languages, are publicly available on Zenodo at: https://zenodo.org/records/19334278.

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