May 16, 2024, 4:46 a.m. | Muhammad Farid Adilazuarda

cs.CL updates on arXiv.org arxiv.org

arXiv:2311.12373v3 Announce Type: replace
Abstract: Significant progress has been made on text generation by pre-trained language models (PLMs), yet distinguishing between human and machine-generated text poses an escalating challenge. This paper offers an in-depth evaluation of three distinct methods used to address this task: traditional shallow learning, Language Model (LM) fine-tuning, and Multilingual Model fine-tuning. These approaches are rigorously tested on a wide range of machine-generated texts, providing a benchmark of their competence in distinguishing between human-authored and machine-authored linguistic …

abstract analysis arxiv beyond challenge comparative analysis cs.cl evaluation generated human human and machine language language model language models machine paper progress replace text text generation turing type

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Senior DevOps Engineer- Autonomous Database

@ Oracle | Reston, VA, United States