arXiv Computation and Language
Sep 1

Whose Assessment of Distress? Community Perspectives and LLM Alignment on Well-Being Posts

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 Machine Learning
Jun 5

RedditPersona: A Modular Framework for Community-Conditioned LLM Adaptation from Reddit

arXiv:2606. 06027v1 Announce Type: cross Abstract: Community-conditioned language model adaptation requires choices about data collection, community definition, and evaluation that are currently made independently in each study, making it hard to compare assumptions or reuse artifacts.

By Amirhossein Ghaffari, Ali Goodarzi, Huong Nguyen, Simo Hosio, Lauri Lov\'en, Ekaterina Gilman
arXiv AI
4d ago

ThreadBench: Benchmarking Realism in Agentic Simulation of Real-world Discussions

ThreadBench is a benchmark that evaluates how well large language model (LLM) agents simulate real-world Reddit discussions, using 9,817 real threads across 8 domains and 116 subdomains. It compares generated and real threads on 12 metrics grouped into uniformity, expression, tone, and interaction. The authors also introduce Thread-Prompt-based Discussion Generation (ThreadPDG), a Planner-Writer framework that learns thread growth from real seeds and achieves high pass rates on ThreadBench, outperforming baseline methods and matching real discussions in 78.7% of human evaluations.

By Yaoning Yu, Ye Yu, John Sciarappo, Haojing Luo, Kai-min Chang, Yi-Chia Wang, Haohan Wang
arXiv AI
Sep 21

Aligning with Lived Experience: Heterogeneous Benefits of Fine Tuning in Mental Health Support Generation

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