Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Kumarage, Tharindu"'
Large Language Models (LLMs) are proficient at generating coherent and contextually relevant text but face challenges when addressing knowledge-intensive queries in domain-specific and factual question-answering tasks. Retrieval-augmented generation
Externí odkaz:
http://arxiv.org/abs/2407.12216
Autor:
Ai, Lin, Kumarage, Tharindu, Bhattacharjee, Amrita, Liu, Zizhou, Hui, Zheng, Davinroy, Michael, Cook, James, Cassani, Laura, Trapeznikov, Kirill, Kirchner, Matthias, Basharat, Arslan, Hoogs, Anthony, Garland, Joshua, Liu, Huan, Hirschberg, Julia
The proliferation of Large Language Models (LLMs) poses challenges in detecting and mitigating digital deception, as these models can emulate human conversational patterns and facilitate chat-based social engineering (CSE) attacks. This study investi
Externí odkaz:
http://arxiv.org/abs/2406.12263
Content moderation faces a challenging task as social media's ability to spread hate speech contrasts with its role in promoting global connectivity. With rapidly evolving slang and hate speech, the adaptability of conventional deep learning to the f
Externí odkaz:
http://arxiv.org/abs/2404.11036
Large language models (LLMs) excel in many diverse applications beyond language generation, e.g., translation, summarization, and sentiment analysis. One intriguing application is in text classification. This becomes pertinent in the realm of identif
Externí odkaz:
http://arxiv.org/abs/2403.08035
Autor:
Kumarage, Tharindu, Agrawal, Garima, Sheth, Paras, Moraffah, Raha, Chadha, Aman, Garland, Joshua, Liu, Huan
We have witnessed lately a rapid proliferation of advanced Large Language Models (LLMs) capable of generating high-quality text. While these LLMs have revolutionized text generation across various domains, they also pose significant risks to the info
Externí odkaz:
http://arxiv.org/abs/2403.01152
The contemporary LLMs are prone to producing hallucinations, stemming mainly from the knowledge gaps within the models. To address this critical limitation, researchers employ diverse strategies to augment the LLMs by incorporating external knowledge
Externí odkaz:
http://arxiv.org/abs/2311.07914
In recent years, there has been a rapid proliferation of AI-generated text, primarily driven by the release of powerful pre-trained language models (PLMs). To address the issue of misuse associated with AI-generated text, various high-performing dete
Externí odkaz:
http://arxiv.org/abs/2310.05095
Large language models (LLMs) are increasingly being used for generating text in a variety of use cases, including journalistic news articles. Given the potential malicious nature in which these LLMs can be used to generate disinformation at scale, it
Externí odkaz:
http://arxiv.org/abs/2309.03992
Autor:
Kumarage, Tharindu, Bhattacharjee, Amrita, Padejski, Djordje, Roschke, Kristy, Gillmor, Dan, Ruston, Scott, Liu, Huan, Garland, Joshua
The rapid proliferation of AI-generated text online is profoundly reshaping the information landscape. Among various types of AI-generated text, AI-generated news presents a significant threat as it can be a prominent source of misinformation online.
Externí odkaz:
http://arxiv.org/abs/2309.03164
Autor:
Kumarage, Tharindu, Liu, Huan
Large language models (LLMs) such as GPT-4, PaLM, and Llama have significantly propelled the generation of AI-crafted text. With rising concerns about their potential misuse, there is a pressing need for AI-generated-text forensics. Neural authorship
Externí odkaz:
http://arxiv.org/abs/2308.07305