no code implementations • 7 Jan 2025 • Yuxi Xia, Pedro Henrique Luz de Araujo, Klim Zaporojets, Benjamin Roth
Concretely, we build Calib-n, a novel framework that trains an auxiliary model for confidence estimation that aggregates responses from multiple LLMs to capture inter-model agreement.
1 code implementation • 26 Aug 2024 • Loris Schoenegger, Yuxi Xia, Benjamin Roth
However, the quality of different explanation methods has not previously been assessed for detectors of MGT.
1 code implementation • 4 Jul 2024 • Yuxi Xia, Kilm Zaporojets, Benjamin Roth
A diverse range of large language models (LLMs), e. g., ChatGPT, and visual question answering (VQA) models, e. g., BLIP, have been developed for solving textual and visual question answering tasks.
no code implementations • 5 May 2024 • Yuxi Xia, Anastasiia Sedova, Pedro Henrique Luz de Araujo, Vasiliki Kougia, Lisa Nußbaumer, Benjamin Roth
Finally, the prompt performance of detecting model memorization is quantified by the percentage of name pairs for which the model has higher confidence for the name from the training set.
no code implementations • 13 Mar 2024 • Benjamin Roth, Pedro Henrique Luz de Araujo, Yuxi Xia, Saskia Kaltenbrunner, Christoph Korab
Machine learning (ML) and artificial intelligence (AI) approaches are often criticized for their inherent bias and for their lack of control, accountability, and transparency.
1 code implementation • 17 Aug 2020 • Buse Gul Atli, Yuxi Xia, Samuel Marchal, N. Asokan
In this paper, we present WAFFLE, the first approach to watermark DNN models trained using federated learning.