United States

Conspiracy theories widely spread on social media and can harm society 
by increasing mistrust, vaccine hesitancy, and political radicalization. 
However, most automated detection systems have traditionally relied on 
topic-specific classifiers, which often struggle to generalize across 
domains and provide little explanation for why a text is considered 
conspiratorial. To address these limitations, this paper explores various 
LLMs on the SemEval-2026 Task 10: psycholinguistic conspiracy marker 
extraction and binary conspiracy detection from Reddit submission 
statements. Specifically, we adopt a training-free few-shot prompting 
approach using different instruction-tuned large language models under a 
variety of few-shot settings (k in {0,1,5,10,15, 20}). Within this 
framework, the proposed prompting strategy incorporates psychology-informed 
instructions to guide the models in identifying conspiracy-related signals. 
As a result, the presented system achieves an F1 score of 0.21 for marker 
extraction and 0.81 for conspiracy detection, ranking 16th out of 30 teams 
in Subtask~1 and 36th out of 52 in Subtask~2 without any task-specific 
fine-tuning. These results suggest that psycholinguistically grounded 
prompting can support interpretable conspiracy analysis; however, challenges 
remain in identifying implicit markers.

ACL 2026

CUET_320 at SemEval-2026 Task 10: Few-Shot Large Language Models for Psycholinguistic Marker Extraction and Conspiracy Detection

psycholinguistic analysis

conspiracy detection

few-shot prompting

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workshop paper

Despite the need to address the proliferation of conspiracy theories in online discussions, there is a lack of benchmarks for effectively detecting conspiracy-related content in everyday conversational settings. We introduce a novel dataset of comments from Reddit, ranging from politics to TV series, as well as two synergetic tasks: (1) extracting five psycholinguistic markers, grounded in evolutionary psychology, and (2) detecting conspiracy content. The data enable multi-task approaches, allowing testing of whether marker extraction improves detection performance.

SemEval-2026 Task 10: PsyCoMark -- Psycholinguistic Conspiracy Marker Extraction and Detection

We present our shared task on evaluating the adaptability of LLMs and NLP systems across multiple languages and cultures. The task data consist of an extended version of our manually constructed BLEnD benchmark (Myung et al., 2024), covering more than 30 language--culture pairs, predominantly representing low-resource languages spoken across multiple continents. As the task is designed strictly for evaluation, participants were not permitted to use the data for training, fine-tuning, few-shot learning, or any other form of model modification.

Our task includes two tracks: (a) Short-Answer Questions (SAQ) and (b) Multiple-Choice Questions (MCQ). Participants were required to predict labels and were allowed to submit any NLP system and adopt diverse modelling strategies, provided that the benchmark was used solely for evaluation. The task attracted more than 140 registered participants, and we received final submissions from 62 teams, along with 19 system description papers.

We report the results and present an analysis of the best-performing systems and the most commonly adopted approaches. Furthermore, we discuss shared insights into open questions and challenges related to evaluation, misalignment, and methodological perspectives on model behaviour in low-resource languages and for under-represented cultures. Our data and resources are available at https://github.com/BLEnD-SemEval2026/SemEval-2026-Task-7.

SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures

Medical query rewriting transforms verbose
consumer health questions into concise clin-
ical queries, a critical step in health infor-
mation retrieval. Large language models
(LLMs) perform well on this task by stan-
dard metrics, yet high ROUGE or BERTScore
does not guarantee preservation of clinical
content. To address this issue, we intro-
duce MedFaith-F1, a category-level faithful-
ness metric over four clinically salient cate-
gories: clinical problems, medications, proce-
dures, and follow-up intent. We further propose
a hybrid Evidence and Knowledge-Grounded
Retrieval-Augmented Generation (EKG-RAG),
an evidence and knowledge-grounded frame-
work combining hybrid retrieval over PubMed
and MedlinePlus resources with UMLS (Uni-
fied Medical Language System)-aligned ontol-
ogy grounding. Evaluating LLMs LLaMA-
3 and Qwen2.5 across zero-shot, few-shot,
and QLoRA settings on MeQSum and med-
ical question-pair (MQP) datasets revealed
that base models exhibit category-level faith-
fulness failure rates (CHR) exceeding 40%,
invisible to standard metrics, while EKG-
RAG with QLoRA reduces CHR to 26.75%,
achieving MedFaith-F1 of 0.73. Our find-
ings call for faithfulness-aware evaluation
in clinical query rewriting, and MedFaith-
F1 provides a reproducible step in that di-
rection.

Beyond Lexical Similarity: Evaluating Faithfulness in LLM-Based Medical Question Reformulation

This paper presents a hybrid solution for a binary classification of medical PubMed articles created for identifying reports that associate clinical metadata with SARS-CoV-2 genomic sequences. The system is designed to catch the subtle distinction between reports of sequence-associated patient metadata and sentences where such metadata is either unrelated, irrelevant, or linked to previous studies. The biggest challenge is the fact that the medical dataset is highly imbalanced, consisting of only 13.3 % of medical reports labeled positive. Our system proposes a hybrid solution combining four approaches that include dual-evidence tagging, negation-aware suppression, semantic frame extraction, and adversarial training. All these approaches were tested on multiple models: BiomedBERT-baseabstract, BioLinkBERT-large, PubMedBERTbase-fulltext, followed by a best-subset ensemble search to obtain an F1 score of 0.786, setting a new benchmark and positioning the solution in 1st place in the competition.

MetaMiners at SMM4H-HeaRD 2026: A Semantic-StructuralKnowledge-Enriched Ensemble for SARS-CoV-2 Metadata Identification

This paper describes the BIT.UA sys-
tem for the MultiClinNER shared task at
#SMM4H–HeaRD 2026, targeting multi-
lingual clinical named entity recognition
across seven languages for three entity types
(Disease, Procedure, Symptom). We extend
the Multi-Head CRF architecture, originally
developed for multi-class NER on Spanish
clinical text, to the multilingual setting. To
enable joint multi-entity training despite per-
entity text variations in the dataset, we develop
an adaptive text consolidation pipeline that
preserves over 94% of annotations. Our central
finding is that a single xlm-roberta-large model,
trained jointly on all seven languages and
three entity types, achieves competition rank 2
for five of seven languages, outperforming
dedicated monolingual models by up to +6.94
F1 points, while requiring only a single set
of weights. Ensembling multiple seeds of
this model achieves rank 1 for those five
languages, and combining it with monolingual
models yields rank 1 for the remaining
two.

BIT.UA at #SMM4H–HeaRD 2026: Towards Multi-Class Multilingual
Clinical Entity Recognition with Multi-Head CRF Ensembles

Word-level cross-lingual alignment is central to
annotation projection, translation auditing, and
cross-lingual faithfulness estimation, yet exist-
ing neural aligners are rarely adapted to special-
ized domains. In this paper, we introduce Clin-
icalAligner26AM, a large-context multilingual
aligner model for biomedical and clinical text
initialized from ClinicalEncoder26AM (Remy,
2026b). Our training recipe is inspired by
AWESoME Align (Dou and Neubig, 2021). We
build our soft alignment target by sharpening
with Sinkhorn–Knop optimal transport a cost
matrix established for parallel clinical texts and
conversations through the fusion of sentence-
level, phrase-level, and token-level signals. We
distill this sharpened alignment matrix directly
into our student aligner, by encouraging its
naive cosine-based token similarity scores to
match this target. At inference time, we project
source-span scores through the learned token
alignment matrix and decode the longest valid
high-scoring span in the target text, optionally
supported by MultiClinNER predictions sum-
marized in Appendix B. We evaluate CA26AM
on the MultiClinCorpus shared task, which
projects Spanish clinical entity annotations into
six target languages (Gallego-Donoso et al.,
2026; Lopez-Garcia et al., 2026). Our two sub-
mitted systems ranked respectively first and
second across all languages and entity types,
with character-weighted F1 scores above 0.95
in nearly all settings.

Parallia at #SMM4H-HeaRD 2026:
ClinicalAligner26AM: A Cross-Lingual Aligner for Dataset Translation;
Evidences from the MultiClinCorpus Shared Task

We describe two system submissions to Task 4
of the SMM4H-HeaRD 2026 Shared Task
on automated SOAP note generation from
doctor–patient dialogues. Our first submis-
sion is a standalone entity-conditioned gen-
eration model: Mistral-7B-Instruct-v0.1 fine-
tuned with QLoRA on 8,529 MedSynth train-
ing dialogues, where both training and infer-
ence prompts include clinical entities extracted
and grouped by SOAP section. Our second
submission is a four-stage modular pipeline
that additionally incorporates a hybrid retrieval
stage and a rule-based verification stage. The
key finding of this work is that incorporating
structured clinical domain knowledge, in the
form of NER entities grouped by SOAP sec-
tion, directly into the generation prompt pro-
duces consistent and reliable improvements
over dialogue-only generation. Our four-stage
pipeline submission achieved an average score
of 0.54 on the official test set, ranking first on
the shared task leaderboard.

NU_DeepHealthNLP at #SMM4H-HeaRD 2026: Entity-Conditioned
Generation and a Four-Stage Pipeline for Automated SOAP Note
Generation

TNM cancer staging is a critical process for
characterizing tumor burden and guiding clini-
cal decisions. Nevertheless, its automated ex-
traction remains challenging due to the unstruc-
tured and heterogeneous nature of free-text
pathology reports. This paper describes the par-
ticipation of the URJC-Team in Task 6 of the
Social Media Mining for Health/Health Real-
World Data (#SMM4H-HeaRD) 2026 Shared
Tasks. It focuses on predicting TNM stag-
ing from pathology reports. The proposed
workflow combines hand-crafted regular ex-
pressions with a Large Language Model (LLM).
First, explicit TNM mentions are extracted us-
ing rule-based patterns. Then, any stage not
recovered by these rules is inferred by an LLM.
Overall, the proposal provides competitive re-
sults across all official shared-task phases.

URJC-Team at #SMM4H-HeaRD 2026: TNM Stage Extraction with a
Regex-LLM Workflow

Six tasks from the SMM4H–HeaRD 2026
workshop were addressed with task-specific
large-language-model (LLM) pipelines relying
on prompt engineering, strict structured (JSON)
responses, and deterministic rule sets. The
pipelines utilize no task-specific fine-tuning
and can be adapted across diverse clinical and
social media data. This study demonstrates that
general-purpose LLMs (gpt-5.4-mini and gpt-
5.4) can accurately extract and classify crucial
health information when constrained by strict
output schemas. Notably, our hybrid approach
achieved the best overall performance among
all participating systems for Task 2 (Insomnia
Detection).

MedMind AI at #SMM4H-HeaRD 2026: Data Extraction and Generation Using Prompt Engineering and Structured Outputs (Tasks 1–6)

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.

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