180 citations · 308 across the 7 of their papers we have counts for
10 papers · 1 filter
Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities
Maximilian Mozes, Xuanli He, Bennett Kleinberg +1
Spurred by the recent rapid increase in the development and distribution of large language models (LLMs) across industry and academia, much recent work has drawn attention to safet…
Challenges and Applications of Large Language Models
Jean Kaddour, Joshua Harris, Maximilian Mozes +3
Large Language Models (LLMs) went from non-existent to ubiquitous in the machine learning discourse within a few years. Due to the fast pace of the field, it is difficult to identi…
Identifying Human Strategies for Generating Word-Level Adversarial Examples
Maximilian Mozes, Bennett Kleinberg, Lewis D. Griffin
Adversarial examples in NLP are receiving increasing research attention. One line of investigation is the generation of word-level adversarial examples against fine-tuned Transform…
Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification
Maximilian Mozes, Max Bartolo, Pontus Stenetorp +2
Research shows that natural language processing models are generally considered to be vulnerable to adversarial attacks; but recent work has drawn attention to the issue of validat…
No Intruder, no Validity: Evaluation Criteria for Privacy-Preserving Text Anonymization
Maximilian Mozes, Bennett Kleinberg
For sensitive text data to be shared among NLP researchers and practitioners, shared documents need to comply with data protection and privacy laws. There is hence a growing intere…
The Grievance Dictionary: Understanding Threatening Language Use
Isabelle van der Vegt, Maximilian Mozes, Bennett Kleinberg +1
This paper introduces the Grievance Dictionary, a psycholinguistic dictionary which can be used to automatically understand language use in the context of grievance-fuelled violenc…