Cohere For AI Invited Talk

Apr 11, 2024·
Jason Lucas
Jason Lucas
· 1 min read
Image credit: Cohere
Abstract
In today’s world, the rapid advancement and widespread use of large language models (LLMs) have brought about both opportunities and challenges. While these models have tremendous potential, they also pose risks, particularly in generating harmful and misleading content. Our research introduces an innovative “Fighting Fire with Fire” (F3) strategy to address this issue by leveraging the capabilities of modern LLMs. Our approach utilizes GPT-3.5-turbo to generate both authentic and deceptive content through advanced paraphrase and perturbation techniques. Furthermore, we apply zero-shot in-context semantic reasoning to differentiate genuine from deceptive posts and news articles. Our extensive experiments demonstrate that GPT-3.5-turbo achieves a superior accuracy of 68-72% in detecting disinformation, outperforming previous models. This research underscores the potential of using advanced AI to combat the very problems it may create. In another study, we address the spread of COVID-19 misinformation in low-resource regions, focusing on the Caribbean. Given the lack of abundant fact-checking resources in these areas, we transferred knowledge from fact-checked claims in the US to detect misinformation in English, Spanish, and Haitian French. Our findings highlight the challenges and limitations of current fake news detection methods in low-resource settings and emphasize the need for further research in multilingual detection.
Location

Online Presentation

University Park, Pensylvania 16802-2440

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Jason Lucas
Authors
Jason Lucas
Ph.D. Candidate in Informatics

I am a PhD candidate in Informatics in the College of IST at Penn State University, where I conduct research at the PIKE Research Lab under the guidance of Dr. Dongwon Lee. I specialize in AI/ML research focused on Information Integrity, Safe and Ethical AI, including combating harmful content across multiple languages and modalities. My research spans low-resource multilingual NLP, generative AI, and adversarial machine learning, with work extending across 79 languages. I have published 12 papers with 260+ citations in premier venues including ACL, EMNLP, IEEE, and NAACL.

My doctoral research focuses on bridging the digital language divide through transfer learning, classification (NLU), generation (NLG), adversarial attacks, and developing end-to-end AI pipelines using RAG and Agentic AI workflows for combating multilingual threats. Drawing from my Grenadian background and knowledge of local Creole languages, I bring a global perspective to AI challenges, working to democratize state-of-the-art AI capabilities for underserved linguistic communities worldwide. My mission is to develop robust multilingual multimodal systems and mitigate evolving security vulnerabilities while enhancing access to human language technology through cutting-edge solutions.

As an NSF LinDiv Fellow, I conduct transdisciplinary research advancing human-AI language interaction for social good. I actively mentor 5+ research interns and teach Applied Generative AI courses. Through industry experience at Lawrence Livermore National Lab, Interaction LLC, and Coalfire, I bridge academic research with practical applications in combating evolving security threats and enhancing global AI accessibility. I see multilingual advances and interdisciplinary collaboration as a competitive advantage, not a communication challenge. Beyond research, I stay active through dance, fitness, martial arts, and community service.