United States

Language-based assessments have demonstrated high convergent validity with corresponding mental and physical health constructs, however often fail to address discriminant validity - the measure&#39;s ability to distinguish the target construct from related ones. This is a common phenomenon within the domain of mental health, as well as comorbidity with physical health conditions. Identifying key features of individual dimensions of mental and physical health present in language can unlock new avenues of research for natural language processing and psychology. We propose two augmentations to the objective function of the Ridge model, deriving closed-form solutions compatible with Singular Value Decomposition-based solvers, to enforce discriminant validity of off-target constructs using Mean Squared Error (MSE) and Squared Cosine Similarity (SCS,) both having widespread use in contrastive learning. By varying the discrimination strength, we find that a decrease in 0.005 Pearson correlation points can result in a Pearson correlation point increase upwards of 0.132 in discriminant validity for mental and physical health constructs derived from self-reported questionnaires. We see similar improvements across multiple fundamental psychopathology dimensions simultaneously, increasing discriminant validity by 0.012 with stronger increases coming from more noisy, less reliable constructs. Our contributions provide a theoretically grounded path towards improving confidence in language-based assessments in the clinical sector, improving specificity of said assessments to various areas of health.

ACL 2026

Discriminant Validity: Disentangling Health and Emotional Constructs from Language-Based Assessments

Language-based assessments have demonstrated high convergent validity with corresponding mental and physical health constructs, however often fail to address discriminant validity - the measure's ability to distinguish the target construct from related ones. This is a common phenomenon within the domain of mental health, as well as comorbidity with physical health conditions. Identifying key features of individual dimensions of mental and physical health present in language can unlock new avenues of research for natural language processing and psychology. We propose two augmentations to the objective function of the Ridge model, deriving closed-form solutions compatible with Singular Value Decomposition-based solvers, to enforce discriminant validity of off-target constructs using Mean Squared Error (MSE) and Squared Cosine Similarity (SCS,) both having widespread use in contrastive learning. By varying the discrimination strength, we find that a decrease in 0.005 Pearson correlation points can result in a Pearson correlation point increase upwards of 0.132 in discriminant validity for mental and physical health constructs derived from self-reported questionnaires. We see similar improvements across multiple fundamental psychopathology dimensions simultaneously, increasing discriminant validity by 0.012 with stronger increases coming from more noisy, less reliable constructs. Our contributions provide a theoretically grounded path towards improving confidence in language-based assessments in the clinical sector, improving specificity of said assessments to various areas of health.

workshop paper

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>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
<html><button style="display: inline-flex; align-items: center; justify-content: center; white-space: nowrap; border-radius: 9999px; font-weight: bold; background: #7c3aed; color: white; font-family: 'Space Grotesk', sans-serif; height: 40px; font-size: 16px; padding: 0 20px; border: none; cursor: pointer" onclick="window.open('https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing')">Conference Handbook</button></html>
<br><br><br><br>

To access this event page, you need to log in with the **email address you registered with**. <br>Access credentials will be sent to your email from Underline -  subject line "Welcome to ACL 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://2026.aclweb.org/registration/) first.

**Online Registration Form**: https://acl.swoogo.com/acl2026

Registration Required

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Cognitive distortions, distorted patterns of thinking, have been increasingly studied in computational mental health research. Although they are related to many, if not all, mental health disorders, most existing studies focus primarily on depression. In this work, we explore distortion profiles across multiple mental health conditions. We analyzed a large Reddit-based dataset containing posts from nine self-reported mental health groups as well as a control group using both an n-gram-based method and a fine-tuned transformer model for detecting cognitive distortions. Mental health groups, both when pooled together and when examined individually, showed higher prevalence of cognitive distortions compared to the control group, with the effect sizes ranging from small to moderate. When comparing distortion profiles across conditions, we observed largely similar patterns, although some groups exhibited overall higher levels of distortions than others. These findings suggest that relatively simple lexical approaches can be useful for exploratory analyses of group-level trends in large-scale mental health text data.

Exploring Profiles of Cognitive Distortions Associated with Mental Health Disorders

Person-level psychological assessment requires aggregating meaning across many messages from the same individual, a task that document-level training objectives were not explicitly designed for. 
We present a systematic, empirical comparison between architecturally matched traditional (a) base-transformers and (b) document-tuned-transformers (further contrastively fine-tuned at the document-level, sometimes referred to as "sentence transformers") under otherwise identical conditions. 
Comparing layer-wise and overall performance across two longitudinal mental health and psychological datasets, 
we find document-tuned models demonstrated a consistent improvement over base representations (increase in Pearson r of 13.4%, p=.015). 
Robustness analyses revealed document-tuned models remained more accurate under perturbations to word deletion, synonym replacement, typo injection, and back translation. 
Further, hedged language (e.g., 'usually') was more characteristic of outcomes in document-tuned embeddings while abundance (e.g., 'lot') was more characteristic of base-transformers, suggesting document-tuned models may better capture uncertainty.
These results suggest representation choice impacts mental health prediction, document-tuned models often being more adept.

Evaluating Document-Tuned Transformer Representations for Person-level Mental Health Assessment

Suicide is a major public health concern, underscoring the importance of understanding communication practices used in crisis intervention. Text-based crisis services are increasingly used, yet little is known about how counselors construct messages across encounters. One understudied feature of this setting is counselor text reuse, or the repeated use of identical or highly similar message content across different clients. Although reuse may support efficiency and consistency, it may raise questions about how personalised responses are across counselors. This study provides a descriptive analysis of counselor text reuse in a large dataset of 4.7 million messages of real-time text-based crisis counseling conversations. Across 136 counselors, mean message similarity was very low, indicating little overall text reuse for most counselors. However, 103 counselors showed at least one instance of detected reuse, and a smaller subset demonstrated more consistent reuse. Reuse was also positively associated with counselor encounter volume across measures of reuse. Frequently reused longer passages primarily involved structured coping-oriented or psychoeducational content, such as coping strategies, grounding exercises, self-care tips, and relaxation techniques. The findings suggest that counselor text reuse increased with encounter volume, but average levels of reuse were low across counselors and they provide a foundation for future work examining associations with service delivery and client outcomes.

"How'd You Type That So Fast?" A Descriptive Analysis of Counselor Message Text Reuse in Text-Based Crisis Counseling

Some psychotherapies, such as written exposure therapy for posttraumatic stress disorder, utilize "scripts"' during parts of treatment, but verifying script adherence to ensure engagement of key mechanisms of change is a time-consuming step for therapy supervisors. Here, we formalize therapy script adherence as an NLP task, and evaluate several simple (text similarity) and more complex (few-shot LLM) approaches. Over 351 annotated therapist utterance-script pairs, we find text similarity approaches to be highly competitive with LLMs and produce fewer false positives. ROUGE-L recall achieves F1 = 0.973, and BLEU achieves F1 = 0.972 with full precision and zero false positives. GPT-5.2 achieves F1 = 0.935 and GPT-4o-mini achieves F1 = 0.876. Given that the text similarity techniques are multiple orders of magnitude less complex, our results underscore the ability of simpler NLP techniques to still be effective in the age of LLMs for tasks that are more textual in nature and suggest their utility in clinical training for giving supervisees rapid, privacy-preserving feedback on their delivery of evidence-based psychotherapy scripts.

Language-Based Detection of Adherence to Evidence-Based Psychotherapy Scripts

Recent advances in Large Language Models
(LLMs) have motivated their adoption across
a wide range of domains, including Artificial
Intelligence (AI) for mental health. Given the
growing prevalence of mental health disorders

worldwide and the limited accessibility of pro-
fessional care, there is an increasing demand

for scalable computational approaches that can

assist in early detection and continuous mon-
itoring of psychological well-being. In this

area, ongoing efforts have focused on curating
domain-specific datasets and leveraging them
to develop LLMs capable of supporting holistic

mental health analysis. In line with this direc-
tion, we propose an LLM-based pipeline for

comprehensive mental health analysis over se-
quentially ordered user posts, as part of the

CLPsych shared task. Our pipeline offers a uni-
fied framework that jointly enables post-level

assessment and user-level temporal modeling.

Team MKC at CLPsych 2026: Capturing and Characterizing Mental Health Changes through Social Media Timeline Dynamics

Targeting the CLPsych 2026 (Ali et al., 2026)

shared task, this study proposes a unified frame-
work based on three complementary strategies.

We implement a pipeline integrating post-level
psychological state recognition, timeline-level

change detection, and evidence-based clini-
cal summarization. For Task 1, we evaluate

three paradigms for extracting ABCD elements:
zero-shot prompting, Qwen-3 fine-tuning, and
TextCNN-based hierarchical classification. For
Task 2, we design a cascaded temporal model
that combines Mental-BERT features, log-time
encoding, and Bi-LSTM to capture mental state

switches and escalations under irregular post-
ing intervals. For Task 3, we adopt a dynamic

retrieval-augmented generation strategy to pro-
duce structured clinical summaries. Experimen-
tal results show that our system ranks second on

the official leaderboard for Task 2, demonstrat-
ing the effectiveness of time-aware modeling

for change detection. Error analysis further

reveals over-prediction bias and valence inver-
sion in Tasks 1 and 3, highlighting pipeline

error propagation as a key challenge for future
work.

A Multi-Strategy Fusion Framework for Dynamic Mental State Modeling

Social media posts are a rich and valuable
source of data for analyzing mental health
states and users’ well-being using automated
analysis tools. In this work, we demonstrate

how we used a range of Natural Language Pro-
cessing (NLP) methods, including Long Short-
Term Memory (LSTM), BERT-based models,

and Large Language Models (LLMs), for self-
state and well-being analysis and summariza-
tion during the CLPsych Shared Task 2026.

Our approach achieved one of the top Consis-
tency and Contradiction scores for the summa-
rization task and also middle-level results for

the other tasks. By testing and developing such

mental health-state estimation systems, we con-
tributed to improving mental health support

systems. We make our code available1
.

psytechlab at CLPsych 2026: Utilising Natural Language Processing methods and Large Language Models for Social Media Text Analysis

We address the CLPsych 2026 Shared Task on

modeling psychological self-states from lon-
gitudinal social media data. We propose (i) a

hierarchical multi-stage framework that inte-
grates a multi-task transformer encoder and (ii)

a four stage instruction-tuned large language

model finetuning pipeline for subelement clas-
sification, presence estimation, and evidence

extraction. Our approach incorporates element-
conditioned label masking and cross-stage en-
coder transfer, enabling structured prediction

aligned with the ABCD psychological frame-
work. Experiments show improvements over

the baseline on the development setup, with
RoBERTa achieving an 8.3% gain in macro-F1
and improved root mean square error (RMSE),
while a fine-tuned Qwen3 model attains the

best overall performance. These results demon-
strate the effectiveness of combining hierarchi-
cal multi-task learning with structured gener-
ation for interpretable mental health analysis.

All code for this task is publicly available in a
GitHub repository1

Hierarchical Multi-Stage Modeling of Adaptive and Maladaptive Self-States in Social Media Timelines

The automatic detection of mental health dy-
namics represents a challenging and socially

relevant task, requiring models to capture sub-
tle psychological changes over time. Large

language models have demonstrated remark-
able capabilities in clinical and diagnostic con-
texts, yet their outputs often lack consistency

and may diverge significantly across different
model families and prompting strategies. This
paper presents the USAI team’s submission
to the CLPsych 2026 Shared Task, targeting
Tasks 1.1, 1.2, 2, and 3.1. We propose an
ensemble-based approach combining multiple
open-source large language models, where the

contribution of each model is weighted accord-
ing to its alignment with clinically grounded

human annotations on the training set. Our
system achieves competitive results across the

evaluated subtasks, with particularly strong per-
formance on Tasks 1.2 and 2.

P2P - from Posts to Patterns: An LLM Ensemble Approach to Mental Health Dynamics Detection

This paper presents our prompt-based approach

for modeling mental health timelines from Red-
dit user posts. We address two tasks: identi-
fying moments of change and generating sum-
maries of clinically meaningful changes across

post sequences. Our framework uses large lan-
guage models with in-context learning to an-
alyze self-states and mental health indicators

without task-specific fine-tuning. We build an
inference pipeline with vLLM and Qwen2.5-
72B-Instruct-GPTQ-Int8, and experiment with
few-shot prompting, and balanced few-shot
sampling. We also examine how the number
of visible posts affects the model’s ability to
capture temporal changes. Our results suggest
that prompt-based methods provide a practical
and competitive baseline in low-resource and
sensitive mental health settings, particularly for
modeling self-state dynamics and generating
summaries of psychological change over time.

Downloads

Next from ACL 2026

Exploring Profiles of Cognitive Distortions Associated with Mental Health Disorders

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from ACL 2026

Exploring Profiles of Cognitive Distortions Associated with Mental Health Disorders

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads