The study evaluates prompt evolution techniques for predicting stress and emotion from social media text. Six methods were compared on two model sizes, revealing that GEPA and its extension GEPA+merge consistently improved predictions over initial prompts. Five key prompting strategies—focusing on emotional evidence, distinguishing current from past distress, illustrating indirect cues, requiring emotional context, and guiding label selection—were linked to development gains, while four observations highlighted task-specific and evaluation limitations.
Early stress detection and risk prediction are crucial for timely mental health assessment and support. While large language models have recently shown promise for social media stress prediction, rely...
arXiv:2606. 16687v1 Announce Type: new Abstract: Modeling dimensional affect in longitudinal text requires distinguishing current affect estimation from future affective change forecasting.
PromptSense introduces a sentiment‑aware mental health chatbot that incorporates sentiment and depression analysis with rule‑based filtering before model inference. By dynamically constructing prompts from emotional and risk insights, the system enhances contextual relevance, empathy, and safety of responses. Experiments across four model strategies and two prompt variants show that the engineered prompts outperform DSFT and RLAIF by 1.8% and 1.1% on qualitative metrics and toxicity assessments.
The paper introduces the COmmunity-centered Peer Engaged Support (COPES) dataset and a three‑axis evaluation framework to gauge how well Large Language Models (LLMs) align with community perspectives on mental‑health support queries. Experiments show that fine‑tuning LLMs on COPES improves strategy alignment and emotion‑tone alignment by over 50% for general‑purpose models, yet these gains are uneven across subreddits and coping strategies. The study also finds that post‑training shifts the model’s recommendations toward problem‑focused advice while reducing emotion‑focused responses, indicating persistent disparities in performance across different communities and needs.
arXiv:2606. 10380v1 Announce Type: cross Abstract: Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts.
arXiv:2609.35953v1 Announce Type: new Abstract: Young people increasingly turn to General-Purpose Conversational Agents (GPCAs), such as ChatGPT, in moments of distress. We examine young adults' (age...
arXiv:2604. 07801v2 Announce Type: replace-cross Abstract: Large language models are trained and evaluated on quantitative reasoning tasks written in clean, emotionally neutral language.
arXiv:2610.27603v1 Announce Type: new Abstract: Empathetic interaction involves two distinguishable capacities: representing another person's emotional state (cognitive empathy) and generating an app...
arXiv:2607.23648v2 Announce Type: replace Abstract: Using large language models (LLMs) to assist psychological counseling is an important task in the field of natural language processing. The constru...
The authors present the Cross-Platform Fairness Evaluation (CPFE) framework, a five‑axis audit protocol that assesses discriminative performance, calibration, statistical significance, prediction equity, and attribution stability of transformer models. Applying CPFE to four models trained on a Kaggle mental‑health corpus and tested on Reddit and Twitter, they find substantial cross‑platform degradation in AUC (30–40%) and severe calibration failures (ECE rising to 0.5 on Twitter). The study demonstrates that platform‑specific temperature scaling can largely fix calibration without harming discrimination, while prediction equity and attribution stability analyses reveal significant disparities and vocabulary divergence across platforms. The results argue that cross‑platform validation across all CPFE axes should become a standard requirement for mental‑health NLP systems deployed in heterogeneous environments.
arXiv:2609.22119v1 Announce Type: cross Abstract: Evaluation awareness poses an unprecedented threat to model evaluation, but the mechanisms by which models detect it remain unknown. This study focus...
arXiv:2607. 02885v1 Announce Type: cross Abstract: Cognitive Behavioral Therapy (CBT) provides a structured framework for understanding a user's mental state by examining the interaction between cognitive and behavioral factors.