11 citations · 25 across the 9 of their papers we have counts for
5 papers · 1 filter
Investigating the Vulnerability of LLM-as-a-Judge Architectures to Prompt-Injection Attacks
Narek Maloyan, Bislan Ashinov, Dmitry Namiot
Large Language Models (LLMs) are increasingly employed as evaluators (LLM-as-a-Judge) for assessing the quality of machine-generated text. This paradigm offers scalability and cost…
Prompt Injection Attacks in Defended Systems
Daniil Khomsky, Narek Maloyan, Bulat Nutfullin
Large language models play a crucial role in modern natural language processing technologies. However, their extensive use also introduces potential security risks, such as the pos…
Trojan Detection in Large Language Models: Insights from The Trojan Detection Challenge
Narek Maloyan, Ekansh Verma, Bulat Nutfullin +1
Large Language Models (LLMs) have demonstrated remarkable capabilities in various domains, but their vulnerability to trojan or backdoor attacks poses significant security risks. T…
DN at SemEval-2023 Task 12: Low-Resource Language Text Classification via Multilingual Pretrained Language Model Fine-tuning
Daniil Homskiy, Narek Maloyan
In recent years, sentiment analysis has gained significant importance in natural language processing. However, most existing models and datasets for sentiment analysis are develope…
DIALOG-22 RuATD Generated Text Detection
Narek Maloyan, Bulat Nutfullin, Eugene Ilyushin
Text Generation Models (TGMs) succeed in creating text that matches human language style reasonably well. Detectors that can distinguish between TGM-generated text and human-writte…