all AI news
Adversarial Attacks and Defense for Conversation Entailment Task
May 2, 2024, 4:47 a.m. | Zhenning Yang, Ryan Krawec, Liang-Yuan Wu
cs.CL updates on arXiv.org arxiv.org
Abstract: Large language models (LLMs) that are proved to be very powerful on different NLP tasks. However, there are still many ways to attack the model with very low costs. How to defend the model becomes an important problem. In our work, we treat adversarial attack results as a new (unseen) domain of the model, and we frame the defending problem into how to improve the robustness of the model on the new domain. We focus …
abstract adversarial adversarial attacks arxiv attacks conversation costs cs.ai cs.cl defense however language language models large language large language models llms low nlp results tasks type work
More from arxiv.org / cs.CL updates on arXiv.org
Jobs in AI, ML, Big Data
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
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US