arXiv:2606. 29726v1 Announce Type: cross Abstract: With the rapid development of Artificial Intelligence Generated Content (AIGC) platforms, users increasingly show cross-platform usage intentions.
By Xuchao Zhang, Jihye Lee
The study examines how large language models (LLMs) predict the emotional impact of outcomes in gambling and stock-trading scenarios and whether these predictions influence their decision-making. When outcomes are evaluated alone, LLMs show diminishing sensitivity to magnitude—similar to human value functions—but they amplify gains relative to losses, a phenomenon termed silicon gain amplification. When outcomes are compared directly, the ratings become linear, and in choice tasks the gain-loss asymmetry largely disappears, with the models’ affective ratings not providing a better explanation of choices than monetary utility alone.
By Joseph Marks (NatWest Group, London, UK), Marius C. Vollberg (University of Zurich, Zurich, Switzerland, Swiss Center for Affective Sciences, Geneva, Switzerland)
arXiv:2608.21242v1 Announce Type: new
Abstract: As conversational companions, large language models (LLMs) often have access to users' emotional states. We study how this affective context modulates...
By Jiayi Li, Sanjana Menon, Brett Frischmann, Shomir Wilson, Sarah Rajtmajer
arXiv:2609.23264v1 Announce Type: new
Abstract: Peer-review evaluation is increasingly being automated with LLM-as-a-judge metrics, but this creates a measurement risk. A review may receive a high sc...
By Shakiba Amirshahi, Sajad Ebrahimi, Hai Son Le, Negar Arabzadeh, Ebrahim Bagheri
arXiv:2504. 14053v2 Announce Type: replace-cross Abstract: Rating systems on accommodation platforms suffer from a familiar problem: nearly every listing displays a nearly perfect score, so the number that is supposed to separate good listings from bad ones barely varies.
By Ali Safari
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