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.
By SeungHyun Yi, Seungjun Yi, Sriram Sattiraju, Haeun Park, Joakim Nguyen, Terence Lim, Joseph Skrovan, Mehak Beri, Hitakshi Modi, Eloy Carrion, Andrew Well, Carlos M. Mery, Alexander Rasgon, Mia K Markey, Ying Ding
arXiv:2606. 10380v1 Announce Type: cross Abstract: Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts.
By Grace Byun, Abigail Lott, Rebecca Lipschutz, Sean T. Minton, Elizabeth A. Stinson, Jinho D. Choi
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.
By Sugi Lee, Jianlong Zhou, Jing sUN
arXiv:2606. 16687v1 Announce Type: new Abstract: Modeling dimensional affect in longitudinal text requires distinguishing current affect estimation from future affective change forecasting.
By Sadia Noor, Seemab Latif, Raja Khurram Shahzad, Mehwish Fatima
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.
By Mohit Chandra, Nabin Kim, Eli Min, Aamogh Sawant, Tanmay Sutar, Munmun De Choudhury
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...
By Kaitong Weng, Lixin Liu, Zihao Liu, Bo Wang, Shiguang Ni
The study examines how large language models (LLMs) predict depression scores from language responses. In a "Mirror" setup, participants answered structured diagnostic interviews that the LLMs used to predict scores, yielding near-perfect predictions. In a "Non-Mirror" setup, participants gave life history interviews; the LLMs still achieved outstanding prediction accuracy, and both conditions correlated similarly with PHQ-9 scores, indicating that the Mirror advantage disappears when predicting an independent measure. Topic modeling showed different depression themes across interview types, suggesting Mirror evaluations are more about reliability than validity and that Non-Mirror approaches may enhance clinical relevance.
By Tong Li, Rasiq Hussain, Mehak Gupta, Joshua R. Oltmanns
arXiv:2403.17612v3 Announce Type: replace
Abstract: Labeling corpora constitutes a bottleneck to create models for new tasks or domains. Large language models mitigate the issue with automatic corpus...
By Christopher Bagdon, Prathamesh Karmalker, Harsha Gurulingappa, Roman Klinger
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.
By Terumi Chiba, Yang Luo, Ziyun Cui, Yongsheng Tong, Chao Zhang
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.
By Yisong Chen, Yifan Gao, Sijing Yu, Chuqing Zhao, Yang Lu
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.
By Rajveer Singh Pall, Sameer Yadav
arXiv:2610.33586v1 Announce Type: new
Abstract: Depression severity estimation is crucial for mental health assessment and intervention, yet traditional approaches depend on clinical interviews or se...
By Nada Xu, Xueqin Chen, Qiang Gao, Hongru Liang, Wenqiang Lei