arXiv:2608.10503v2 Announce Type: replace
Abstract: As Large Language Models (LLMs) are increasingly deployed as autonomous agents, accurately evaluating their latent values and biases is critical. T...
By Davood Wadi, Mohsen Ghodrat, Matthew Philp
arXiv:2607. 26062v1 Announce Type: cross Abstract: Background: This work investigates the presence of implicit bias in Large Language Model (LLM)-based chat AI models directed toward people with intellectual disabilities (ID).
By Karly V. Coffey, Gloria L. Krahn, John P. Hanley, Jacob E. Neely
arXiv:2607. 05554v1 Announce Type: cross Abstract: Survey-style evaluations of large language models often treat a prompted response as a measure of a model's values or beliefs.
By Sadia Kamal, Arefa Patwary, Anthony Marchiafava, Atriya Sen, Sagnik Ray Choudhury
arXiv:2610.10232v1 Announce Type: new
Abstract: Large language models (LLMs) have already been shown to match or exceed human experts in persuasion. While their persuasive capabilities hold promise f...
By Kamile Dementaviciute, Julija Vaitonyte, Tijl De Bie
arXiv:2603. 13891v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for automated text annotation in tasks ranging from academic research to content moderation and hiring.
By Petter T\"ornberg
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