April 22, 2024, 4:46 a.m. | Oana Ignat, Xiaomeng Xu, Rada Mihalcea

cs.CL updates on arXiv.org arxiv.org

arXiv:2404.12938v1 Announce Type: new
Abstract: Deceptive reviews are becoming increasingly common, especially given the increase in performance and the prevalence of LLMs. While work to date has addressed the development of models to differentiate between truthful and deceptive human reviews, much less is known about the distinction between real reviews and AI-authored fake reviews. Moreover, most of the research so far has focused primarily on English, with very little work dedicated to other languages. In this paper, we compile and …

abstract arxiv cs.ai cs.cl deception detection development generated gpt human llms multilingual performance reviews type work

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US

Research Engineer

@ Allora Labs | Remote

Ecosystem Manager

@ Allora Labs | Remote

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US