arXiv:2605.27388v2 Announce Type: replace
Abstract: Large language models (LLMs) are increasingly used as proxies for computational social analysis, yet faithfully representing the "thick description...
By Nuan Wen, Chanbin Lim, Xuezhe Ma
arXiv:2610.33021v1 Announce Type: cross
Abstract: Large language models (LLMs) increasingly deliver information through personas, but it is unclear whether correcting the facts also makes their frami...
By Nouar AlDahoul, Myles Joshua Toledo Tan, Yasir Zaki
The paper introduces DistressBench, a benchmark comprising 718 English clinician-authored conversations covering 23 self-harm subcategories, each evaluated against a weighted rubric across seven crisis-support dimensions. Models are scored by the fraction of criteria they satisfy, revealing that the best models meet 88.4% of weighted criteria while clinicians achieve 98.7%. The study highlights gaps in crisis response, notably that 35.3% of conversations where crisis is recognized fail to route users to human help, underscoring the need to evaluate models on supportive care rather than mere refusal.
By Drew Rein, Patrick Oathout, Vishal Kumar, Udari Madhushani Sehwag
arXiv:2609.00222v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used as judges for subjective tasks, where annotators disagree and the relevant question is not only how...
By Daniela Occhipinti, Andrea Piergentili, Marco Guerini
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:2606. 06715v1 Announce Type: cross Abstract: We ask whether topic sentiment has a causal effect on perceived political ideology, and whether the answer depends on who assigns the ideology label.
By Upasana Chatterjee