Against the backdrop of violence in police interactions with the U. S.
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
Text-steerable visual encoders condition a frozen Vision Transformer on a natural-language prompt and point at the matching image region, producing prompt-conditioned heatmaps and features. We audit t...
The paper investigates whether large language models (LLMs) can generate synthetic cyberbullying conversations that replicate the social dynamics of real interactions. Using a comprehensive framework, the authors compare authentic dialogues with synthetic ones from GPT, Grok, and LLaMA across structural, linguistic, affective, and temporal dimensions, and conduct human evaluations of realism. Results show that while LLMs preserve high‑level interaction patterns, they systematically distort finer‑grained social phenomena, with model‑specific biases such as GPT’s suppression of harmful content and Grok’s amplification of aggression.
By Arefeh Kazemi, Hamza Qadeer, Sinan Asci, Joachim Wagner, Brian Davis
arXiv:2610.35017v1 Announce Type: new
Abstract: Text-steerable visual encoders condition a frozen Vision Transformer on a natural-language prompt and point at the matching image region, producing pro...
By Seyedarmin Azizi, Parsa Razmara, Sajjad Ghiasvand, Ardalan Aryashad, Tina Khezresmaeilzadeh
The paper reports the first systematic audit of open‑weight large language models (LLMs) in hiring contexts, examining how job‑posting language influences recruiter and job‑seeker simulations across six models. It finds that agentic language lowers recruiter scores for female candidates while communal language mitigates this effect, and that coded‑exclusion language sharply reduces recruiter scores for non‑White candidates and discourages non‑White personas from applying. The study also identifies the explicit demographic label as the main causal factor and proposes a concrete pre‑deployment audit protocol aligned with EU and U.S. regulatory requirements.
By Kosuke Kitahara, Nobuhiro Yamaguchi