arXiv AI
Jun 10
Expert-Level Crisis Detection in Mental Health Conversations
arXiv:2606. 10380v1 Announce Type: cross Abstract: Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts.
Automated Data Enrichment using Confidence-Aware Fine-Grained Debate among Open-Source LLMs for Mental Health and Online Safety
arXiv:2512. 06227v3 Announce Type: replace-cross Abstract: Real-world indicators play an important role in many Natural Language Processing (NLP) applications, such as life events for mental health analysis and risky behaviours for online safety, yet labelling such information is often costly and/or difficult due to its multi-label and dynamic nature.
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arXiv AI
Jun 8
DEFINED: A Data-Efficient Computational Framework for Fine-Grained Creativity Assessment in Debate Scenarios
arXiv:2606. 07226v1 Announce Type: cross Abstract: Human creativity has emerged as a critical competency in the era of large language models.
arXiv AI
Sep 23
Building Trustworthy Mental Health Benchmarks on Bluesky: A Validation-Aware Weak-Supervision Framework
arXiv:2609.22696v1 Announce Type: new Abstract: Decentralized social media platforms create new opportunities and challenges for computational mental health research because data access, moderation,...
arXiv AI
3d ago
Context-Aware Classification and Grading of Sensitive Information in Online Conversational Health Data
arXiv:2601.09717v2 Announce Type: replace-cross Abstract: Online medical consultations contain sensitive health information whose privacy implications depend not only on the entities mentioned but al...
arXiv AI
Aug 20
Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges
The paper reviews how large language models are applied in mental health, covering areas such as social media analysis, clinical conversational agents, therapy support tools, prompt engineering, and multimodal learning. It synthesizes interdisciplinary studies that use social media posts, electronic medical records, and multimodal inputs to detect depression, assess suicide risk, provide personalized therapy, and generate psychoeducational content. The review also discusses advances in model interpretability, annotation strategies, multimodal fusion techniques, and highlights ethical, sociotechnical, and regulatory challenges while proposing frameworks for safe, equitable, and accountable deployment.
arXiv AI
3d ago
Speech-based Psychological Crisis Assessment using LLMs
The paper presents an LLM-based framework for automatically classifying crisis levels in psychological support hotlines, addressing variability in human judgments and staffing constraints. It introduces a paralinguistic injection method that embeds non‑verbal emotional cues into transcripts, allowing the model to consider acoustic nuances. A reasoning‑enhanced training strategy encourages the model to produce diagnostic reasoning chains, which regularizes and improves classification, achieving a macro F1‑score of 0.802 and accuracy of 0.805 in 5‑fold cross‑validation.
Hugging Face Trending Papers
Jul 16
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research. In depression-related datasets, labels are often assigned without structured evidence, symptom-level justification, or traceable alignment with the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR), limiting both transparency and downstream model interpretability.
arXiv AI
Jul 17
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
arXiv:2607. 15202v1 Announce Type: new Abstract: Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research.
arXiv Machine Learning
Jul 2
REALM: Reliable Expertise-Aware Language Model Fine-Tuning from Noisy Annotations
arXiv:2604. 17289v2 Announce Type: replace Abstract: Supervised fine-tuning of large language models relies on human-annotated data, yet annotation pipelines routinely involve multiple crowdworkers of heterogeneous expertise.
arXiv Computation and Language
Sep 17
Structured Claim-Level Discourse Representations for Dense Health Narratives
The paper introduces a structured framework for claim-level discourse analysis in dense health narratives, addressing the limitations of existing topic- or sentiment-based representations. It identifies an average of 13.22 atomic claims per minute in social media health videos and proposes tuples that link each claim to thematic aspects, stance, and multidimensional pragmatic attributes. A benchmark of 1,191 manually annotated claims from 60 videos across four health domains is created, and experiments show that large language models perform well on thematic categorization and stance prediction but struggle with high-dimensional pragmatic profiling, indicating a need for task-specific inference strategies.
arXiv Computation and Language
Sep 1
Graph2Counsel: Clinically Grounded Synthetic Counseling Dialogue Generation from Client Psychological Graphs
Graph2Counsel is a framework that generates synthetic counseling dialogues by leveraging Client Psychological Graphs (CPGs) to encode the relationships among a client’s thoughts, emotions, and behaviors. The system uses a structured prompting pipeline guided by counselor strategies and explores techniques such as Chain‑of‑Thought and Multi‑Agent Feedback to produce 760 realistic sessions from 76 CPGs. Expert evaluation shows the dataset surpasses previous ones in specificity, counselor competence, authenticity, conversational flow, and safety, and fine‑tuning an open‑source model on it improves performance on several counseling benchmarks.
Hugging Face Trending Papers
Jul 20
AHEAD: Advancing Multi-Class Label Aggregation with Interpretable Cross-Annotator Modeling
Crowdsourced labeling provides valuable labeled data for domains across natural language processing, computer vision, and video. Label aggregation aims to infer latent true labels from noisy and biased annotations, with the key lying in annotator reliability estimation.