arXiv:2607. 02539v1 Announce Type: cross Abstract: Digital platforms commonly optimize for satisfaction using signals such as ratings, likes, and sentiment, implicitly treating satisfaction as a proxy for user well-being.
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.
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.
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...
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...
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.
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."
arXiv:2606. 23701v1 Announce Type: cross Abstract: Qualitative product feedback can reveal nuanced user experiences, but its implicit sentiment is difficult to measure.
arXiv:2610.09569v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for emotional support, raising concern that sustained use may draw users away from their real-world...
arXiv:2609.22096v1 Announce Type: new Abstract: Climate campaigns are often evaluated through attention and mobilization, but less is known about the well-being language that accompanies them. Whethe...
arXiv:2608. 06955v1 Announce Type: new Abstract: Large language models (LLMs) are trained on corpora that contain expressions of human judgment about films, books, music, and more.
Large language models (LLMs) are increasingly used for emotional support, raising concern that sustained use may draw users away from their real-world relationships. Yet existing evaluations primarily...
The paper introduces semanticfa, an R package that performs semantic factor analysis on psychometric scales by converting items into language‑model embeddings and factoring their similarities. It demonstrates the method on the 50‑item International Personality Item Pool Big‑Five markers, compares the semantic factors to a large response‑based factor analysis, and shows how the package can interpret factors, flag redundant items, build short forms, and screen new items. The authors propose semantic‑behavioral coherence as a measurable property of a scale, noting that semantic factor analysis complements but does not replace traditional response‑based factor analysis.