The study investigates how large language models (LLMs) assess psychological distress in online posts from six identity‑based communities. Through a perspectivist annotation task, 321 participants provided 9,587 judgments on 1,198 Reddit posts, revealing modest in‑group agreement (OR = 1.18) that varies across communities. When evaluated against these community‑specific labels, open‑weight LLMs consistently over‑estimate distress—achieving only 31–44% accuracy on posts perceived as none‑to‑mild—while newer models like GPT‑5 and Gemini 2.5 Pro show similar inflation, whereas Claude Opus 4 is more conservative.
"whyItMatters":"The findings highlight that miscalibrated distress detection by LLMs can disproportionately impact the very communities they aim to serve, underscoring the need for equitable AI deployment in mental‑health contexts."
By Andrew Aquilina, Xiang Lorraine Li, Yu-Ru Li
arXiv:2609.00491v1 Announce Type: new
Abstract: Communicating across cultures is inherently challenging, especially through culturally dense and ambiguous formats like memes. While people expect larg...
By Hangxiao Zhu, Suliu Qin, Zhuoyan Li, Ming Jiang, Yu Zhang, Meng Xia
arXiv:2609.16997v1 Announce Type: new
Abstract: Crisis sentiment analysis is especially challenging for low-resource languages such as Bangla, where language, context, and public reaction shift rapid...
By Md. Samiul Alim, Mahir Shahriar Tamim, Tanvir Ahmed Khan, Sharjil Khan, Rafia Ferdous Duti, Shahriyar Zaman Ridoy, Mohammad Ali Moni
arXiv:2608.29209v1 Announce Type: new
Abstract: In this paper, we examine how well AI-generated multimodal stories align with the lived practices, relationships, language, values, and visual expectat...
By Millicent Ochieng, Felermino D. M. A. Ali, Elizabeth A. Ankrah, Najeeb Gambo Abdulhamid, Migisha Boyd, Stephanie Nyairo, Mercy Muchai, Samuel Chege Maina, Aditya Vashistha, Anja Thieme, Jacki O'Neill
Crisis sentiment analysis is especially challenging for low-resource languages such as Bangla, where language, context, and public reaction shift rapidly. We introduce UNRESTSENT200K, a Bangla crisis...
The paper introduces BanglaSafe, a benchmark of 879 Bengali prompts that covers 17 culturally grounded harm categories and five prompting conditions. Evaluation of 18 frontier LLMs shows that 53.6% of responses are unsafe or partially unsafe, with 14.7% containing strictly harmful content. The study finds that the writing style within Bengali has a stronger impact on safety than the language switch itself, and that current safety classifiers struggle to reliably evaluate Bengali content.
By Naymul Islam, Nusrat Jahan Lia, Shubhashis Roy Dipta, Sabik Bin Sultan, Abdullah Khan Zehady