Feb. 15, 2024, 5:43 a.m. | Yuhui Shi, Qiang Sheng, Juan Cao, Hao Mi, Beizhe Hu, Danding Wang

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.09199v1 Announce Type: cross
Abstract: With the rapidly increasing application of large language models (LLMs), their abuse has caused many undesirable societal problems such as fake news, academic dishonesty, and information pollution. This makes AI-generated text (AIGT) detection of great importance. Among existing methods, white-box methods are generally superior to black-box methods in terms of performance and generalizability, but they require access to LLMs' internal states and are not applicable to black-box settings. In this paper, we propose to estimate …

abstract abuse academic ai-generated text application arxiv box box ai cs.ai cs.cl cs.lg detection fake fake news generated importance information language language models large language large language models llms pollution sampling text type via words

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Principal Applied Scientist

@ Microsoft | Redmond, Washington, United States

Data Analyst / Action Officer

@ OASYS, INC. | OASYS, INC., Pratt Avenue Northwest, Huntsville, AL, United States