United States

Large language models (LLMs) show promise in generating supportive responses for mental health and counseling applications. However, their responses often lack cultural sensitivity, contextual grounding, and clinically appropriate guidance. This work addresses the gap of how to systematically incorporate domain-specific, clinically validated knowledge into LLMs to improve counseling quality. We utilize and compare two approaches, retrieval-augmented generation (RAG) and a knowledge graph (KG)–based method, designed to support para-counselors. Our KG is constructed manually and clinically validated, capturing causal relationships between stressors, interventions, and outcomes, with contributions from multidisciplinary people. We evaluated multiple LLMs in both settings using BERTScore F1 and SBERT cosine similarity, as well as human evaluation across five metrics, which is designed to directly measure the effectiveness of counseling beyond similarity at the surface level. The results show that KG-based approaches consistently improve contextual relevance, clinical appropriateness, and practical usability compared to RAG alone, demonstrating that structured, expert-validated knowledge plays a critical role in addressing LLMs limitations in counseling tasks.

ACL 2026

Enhancing Mental Health Counseling Support in Bangladesh using Culturally-grounded Knowledge

ai to support para-counselors

knowledge graph for low-resource mental health settings

ai for mental health

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Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in childhood, and its diagnosis relies on assessments combining clinician judgment with standardized rating scales and reports from parents and teachers. While structured instruments such as the Conners' Teacher Rating Scale–Revised Short Form (CTRS-R:S) quantify ADHD-related behaviors, teachers also provide open-ended narratives that may contain complementary signals not captured by structured assessments. However, it remains unclear to what extent teacher narratives encode signals overlooked by rating scales. In this study, we analyze de-identified Turkish teacher evaluation forms collected during clinical ADHD assessments, including both CTRS-R:S scores and open-ended teacher narratives. We compare predictive signals from structured scores and narrative text and identify cases where structured assessments fail to clearly distinguish ADHD from non-ADHD students while narrative-based models capture distinct behavioral patterns. Notably, these cases show minimal overlap with those missed by the narrative model, suggesting that structured and narrative information encode complementary signals. To interpret these differences, we apply a large language model (LLM)-assisted theme discovery pipeline that reveals distinct attention, behavioral, and family-related patterns, highlighting the potential of natural language processing (NLP) to uncover clinically relevant signals from teacher narratives and to complement traditional ADHD screening tools.

When Rating Scales Fall Short: LLM-Assisted Discovery of ADHD Signals in Turkish Teacher Narratives

Social media research on mental health has focused predominantly on detecting and diagnosing conditions at the individual level. In this work, we shift attention to intergroup behavior, examining how two prominent neurodivergent communities, ADHD and autism, adjust their language when engaging with each other on Reddit. Grounded in Communication Accommodation Theory (CAT), we first establish that each community maintains a distinct linguistic profile as measured by the Linguistic Inquiry and Word Count (LIWC) dictionary. We then show that these profiles shift in opposite directions when users cross community boundaries: features that are elevated in one group's home community decrease when its members post in the other group's space, and vice versa, consistent with convergent accommodation. We further demonstrate the practical stakes of this accommodation: classifiers trained to distinguish the two communities from their in-group language lose roughly 30 percentage points of accuracy, falling below chance, when applied to the same users' cross-community posts, confirming that accommodation, not just diagnosis, drives their predictions. Finally, in an exploratory longitudinal analysis around the moment of public diagnosis disclosure, we find that its effects on linguistic style are small and, in some cases, directionally opposite to cross-community accommodation, providing initial evidence that situational audience adaptation and longer-term identity processes may involve
different mechanisms. Our findings contribute to understanding intergroup communication dynamics among neurodivergent populations online and carry implications for community moderation and clinical perspectives on these conditions.

Mirroring Minds: Asymmetric Linguistic Accommodation and Diagnostic Identity in ADHD and Autism Reddit Communities

As Large Language Models (LLMs) demonstrate strong performance on clinical benchmarks, it remains unclear whether this reflects true patient-specific reasoning or reliance on generalized symptom patterns. To address this question, we evaluate LLMs on a counseling competency benchmark to assess their use of patient-specific contextual information. Through controlled experiments with contextual ablations, role framing, Thread-of-Thought (ThoT) prompting, and input perturbations, we find that removing contextual details results in only modest performance drops, and predictions remain relatively stable under input variations. Error analysis further reveals systematic patterns where models favor general clinical associations over context-specific cues, even when such cues are correctly identified during intermediate reasoning. Our findings suggest that achieving passing-level performance may not reflect robust context-sensitive decision-making, revealing a potential gap between benchmark-level clinical competence and reliable utilization of patient-specific contextual information. These results highlight the need for evaluation frameworks that more directly assess context integration in mental health applications.

On the Role of Context in LLM Alignment to Mental Health Counseling Competencies

When large language models simulate patients or clients, they tend to produce cooperative dialogue, premature emotional insight, and rapid resolution. These defaults undermine clinical training, where the pedagogical value lies in sustained difficulty. We describe Clinical Prompt Engineering (CPE), a methodology developed by a multidisciplinary team of clinician-researchers and prompt engineering experts within the [ProjectName] project. CPE encodes clinical knowledge directly into prompt design: each simulated character is constructed through layered psychological profiles, explicit contingency rules linking interactional events to internal states, and enforced non-linear emotional trajectories that resist the model's pull toward resolution. The methodology has been applied across several clinical training simulations involving over 300 participants in formal studies and iterative pilot phases. Each simulated character is embedded within a multi-agent training environment that provides real-time reflective guidance during the interaction and structured, clinically informed feedback afterward. We illustrate the approach through Talking with Lia, a Hebrew-language simulation in which parents practice responding to a seven-year-old child during repeated missile alerts and forced sheltering. The simulation was deployed within the first week of an acute security crisis in Israel in Winter 2026. Of 132 sessions initiated organically through professional networks, 42 were completed; qualitative feedback emphasized the simulation's difficulty as pedagogically meaningful. Because CPE operates at the level of prompt design, it can be developed by clinician-researcher teams and adapted to new populations, developmental stages, and crisis contexts, potentially extending access to expert-informed training beyond the settings where such expertise is typically available. Where much computational work in clinical psychology has focused on classifying mental health states from text, CPE addresses a complementary task: whether clinicians can respond effectively to those states as they shift in real time. The next step is testing whether the skills practiced in simulation transfer to real interactions.

Clinical Prompt Engineering: Encoding Clinical Knowledge into AI Training Simulations - A Crisis Deployment Case Study

Large language models (LLMs) are increasingly applied to automatic personality assessment, yet most prior work relies on coarse binary labels and direct domain-level predictions, limiting interpretability and ignoring the hierarchical facet structure of personality. In this study, we implement a structured prompting approach with three complementary objectives: direct domain-level prediction, fine-grained facet-level prediction, and domain-level prediction informed by facet outputs. All predictions use a five-level ordinal label scheme, capturing a continuum from very low to very high trait expression. Across all prompt types, we adopt an error-guided self-refinement procedure using in-context learning (ICL) to guide the model toward more accurate predictions. Zero-shot prompts assess baseline performance, while one-shot prompts incorporate a single demonstration example selected through the refinement procedure. Our framework evaluates both domain- and facet-level predictions, enabling examination of how prediction granularity and targeted exemplar selection influence LLM inference. By combining hierarchical domain-facet relationships with structured prompting and refinement, this work aims to provide a systematic approach for interpretable and principled LLM-based personality assessment from long-form life narratives.

Facet-Informed Prompting for LLM-Based Personality Assessment: Error-Guided Exemplar Selection and Hierarchical Prediction

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.

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

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.

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