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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.
arXiv:2606. 10380v1 Announce Type: cross Abstract: Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts.
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.
arXiv:2606. 16687v1 Announce Type: new Abstract: Modeling dimensional affect in longitudinal text requires distinguishing current affect estimation from future affective change forecasting.
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: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...