Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Chen, Canyu"'
Model attribution for machine-generated disinformation poses a significant challenge in understanding its origins and mitigating its spread. This task is especially challenging because modern large language models (LLMs) produce disinformation with h
Externí odkaz:
http://arxiv.org/abs/2407.21264
Autor:
Chen, Canyu, Huang, Baixiang, Li, Zekun, Chen, Zhaorun, Lai, Shiyang, Xu, Xiongxiao, Gu, Jia-Chen, Gu, Jindong, Yao, Huaxiu, Xiao, Chaowei, Yan, Xifeng, Wang, William Yang, Torr, Philip, Song, Dawn, Shu, Kai
Knowledge editing techniques have been increasingly adopted to efficiently correct the false or outdated knowledge in Large Language Models (LLMs), due to the high cost of retraining from scratch. Meanwhile, one critical but under-explored question i
Externí odkaz:
http://arxiv.org/abs/2407.20224
Autor:
Chen, Zhaorun, Du, Yichao, Wen, Zichen, Zhou, Yiyang, Cui, Chenhang, Weng, Zhenzhen, Tu, Haoqin, Wang, Chaoqi, Tong, Zhengwei, Huang, Qinglan, Chen, Canyu, Ye, Qinghao, Zhu, Zhihong, Zhang, Yuqing, Zhou, Jiawei, Zhao, Zhuokai, Rafailov, Rafael, Finn, Chelsea, Yao, Huaxiu
While text-to-image models like DALLE-3 and Stable Diffusion are rapidly proliferating, they often encounter challenges such as hallucination, bias, and the production of unsafe, low-quality output. To effectively address these issues, it is crucial
Externí odkaz:
http://arxiv.org/abs/2407.04842
Autor:
Vidgen, Bertie, Agrawal, Adarsh, Ahmed, Ahmed M., Akinwande, Victor, Al-Nuaimi, Namir, Alfaraj, Najla, Alhajjar, Elie, Aroyo, Lora, Bavalatti, Trupti, Bartolo, Max, Blili-Hamelin, Borhane, Bollacker, Kurt, Bomassani, Rishi, Boston, Marisa Ferrara, Campos, Siméon, Chakra, Kal, Chen, Canyu, Coleman, Cody, Coudert, Zacharie Delpierre, Derczynski, Leon, Dutta, Debojyoti, Eisenberg, Ian, Ezick, James, Frase, Heather, Fuller, Brian, Gandikota, Ram, Gangavarapu, Agasthya, Gangavarapu, Ananya, Gealy, James, Ghosh, Rajat, Goel, James, Gohar, Usman, Goswami, Sujata, Hale, Scott A., Hutiri, Wiebke, Imperial, Joseph Marvin, Jandial, Surgan, Judd, Nick, Juefei-Xu, Felix, Khomh, Foutse, Kailkhura, Bhavya, Kirk, Hannah Rose, Klyman, Kevin, Knotz, Chris, Kuchnik, Michael, Kumar, Shachi H., Kumar, Srijan, Lengerich, Chris, Li, Bo, Liao, Zeyi, Long, Eileen Peters, Lu, Victor, Luger, Sarah, Mai, Yifan, Mammen, Priyanka Mary, Manyeki, Kelvin, McGregor, Sean, Mehta, Virendra, Mohammed, Shafee, Moss, Emanuel, Nachman, Lama, Naganna, Dinesh Jinenhally, Nikanjam, Amin, Nushi, Besmira, Oala, Luis, Orr, Iftach, Parrish, Alicia, Patlak, Cigdem, Pietri, William, Poursabzi-Sangdeh, Forough, Presani, Eleonora, Puletti, Fabrizio, Röttger, Paul, Sahay, Saurav, Santos, Tim, Scherrer, Nino, Sebag, Alice Schoenauer, Schramowski, Patrick, Shahbazi, Abolfazl, Sharma, Vin, Shen, Xudong, Sistla, Vamsi, Tang, Leonard, Testuggine, Davide, Thangarasa, Vithursan, Watkins, Elizabeth Anne, Weiss, Rebecca, Welty, Chris, Wilbers, Tyler, Williams, Adina, Wu, Carole-Jean, Yadav, Poonam, Yang, Xianjun, Zeng, Yi, Zhang, Wenhui, Zhdanov, Fedor, Zhu, Jiacheng, Liang, Percy, Mattson, Peter, Vanschoren, Joaquin
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introdu
Externí odkaz:
http://arxiv.org/abs/2404.12241
The ability to accurately identify authorship is crucial for verifying content authenticity and mitigating misinformation. Large Language Models (LLMs) have demonstrated exceptional capacity for reasoning and problem-solving. However, their potential
Externí odkaz:
http://arxiv.org/abs/2403.08213
Autor:
Xie, Chengxing, Chen, Canyu, Jia, Feiran, Ye, Ziyu, Shu, Kai, Bibi, Adel, Hu, Ziniu, Torr, Philip, Ghanem, Bernard, Li, Guohao
Large Language Model (LLM) agents have been increasingly adopted as simulation tools to model humans in applications such as social science. However, one fundamental question remains: can LLM agents really simulate human behaviors? In this paper, we
Externí odkaz:
http://arxiv.org/abs/2402.04559
Autor:
Chen, Canyu, Shu, Kai
Misinformation such as fake news and rumors is a serious threat on information ecosystems and public trust. The emergence of Large Language Models (LLMs) has great potential to reshape the landscape of combating misinformation. Generally, LLMs can be
Externí odkaz:
http://arxiv.org/abs/2311.05656
Autor:
Chen, Canyu, Shu, Kai
The advent of Large Language Models (LLMs) has made a transformative impact. However, the potential that LLMs such as ChatGPT can be exploited to generate misinformation has posed a serious concern to online safety and public trust. A fundamental res
Externí odkaz:
http://arxiv.org/abs/2309.13788
Autor:
Solaiman, Irene, Talat, Zeerak, Agnew, William, Ahmad, Lama, Baker, Dylan, Blodgett, Su Lin, Chen, Canyu, Daumé III, Hal, Dodge, Jesse, Duan, Isabella, Evans, Ellie, Friedrich, Felix, Ghosh, Avijit, Gohar, Usman, Hooker, Sara, Jernite, Yacine, Kalluri, Ria, Lusoli, Alberto, Leidinger, Alina, Lin, Michelle, Lin, Xiuzhu, Luccioni, Sasha, Mickel, Jennifer, Mitchell, Margaret, Newman, Jessica, Ovalle, Anaelia, Png, Marie-Therese, Singh, Shubham, Strait, Andrew, Struppek, Lukas, Subramonian, Arjun
Generative AI systems across modalities, ranging from text (including code), image, audio, and video, have broad social impacts, but there is no official standard for means of evaluating those impacts or for which impacts should be evaluated. In this
Externí odkaz:
http://arxiv.org/abs/2306.05949
Graph anomaly detection has long been an important problem in various domains pertaining to information security such as financial fraud, social spam, network intrusion, etc. The majority of existing methods are performed in an unsupervised manner, a
Externí odkaz:
http://arxiv.org/abs/2305.10668