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pro vyhledávání: '"Kumar, Shachi H"'
Autor:
Kumar, Shachi H, Sahay, Saurav, Mazumder, Sahisnu, Okur, Eda, Manuvinakurike, Ramesh, Beckage, Nicole, Su, Hsuan, Lee, Hung-yi, Nachman, Lama
Large Language Models (LLMs) have excelled at language understanding and generating human-level text. However, even with supervised training and human alignment, these LLMs are susceptible to adversarial attacks where malicious users can prompt the m
Externí odkaz:
http://arxiv.org/abs/2408.03907
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
Autor:
Su, Hsuan, Cheng, Cheng-Chu, Farn, Hua, Kumar, Shachi H, Sahay, Saurav, Chen, Shang-Tse, Lee, Hung-yi
Recently, researchers have made considerable improvements in dialogue systems with the progress of large language models (LLMs) such as ChatGPT and GPT-4. These LLM-based chatbots encode the potential biases while retaining disparities that can harm
Externí odkaz:
http://arxiv.org/abs/2310.11079
Autor:
Su, Hsuan, Kumar, Shachi H, Mazumder, Sahisnu, Chen, Wenda, Manuvinakurike, Ramesh, Okur, Eda, Sahay, Saurav, Nachman, Lama, Chen, Shang-Tse, Lee, Hung-yi
With the power of large pretrained language models, various research works have integrated knowledge into dialogue systems. The traditional techniques treat knowledge as part of the input sequence for the dialogue system, prepending a set of knowledg
Externí odkaz:
http://arxiv.org/abs/2302.05888
Conversational agents have become an integral part of the general population for simple task enabling situations. However, these systems are yet to have any social impact on the diverse and minority population, for example, helping people with neurol
Externí odkaz:
http://arxiv.org/abs/2112.02246
Building multimodal dialogue understanding capabilities situated in the in-cabin context is crucial to enhance passenger comfort in autonomous vehicle (AV) interaction systems. To this end, understanding passenger intents from spoken interactions and
Externí odkaz:
http://arxiv.org/abs/2007.03876
Our senses individually work in a coordinated fashion to express our emotional intentions. In this work, we experiment with modeling modality-specific sensory signals to attend to our latent multimodal emotional intentions and vice versa expressed vi
Externí odkaz:
http://arxiv.org/abs/2007.02038
We are witnessing a confluence of vision, speech and dialog system technologies that are enabling the IVAs to learn audio-visual groundings of utterances and have conversations with users about the objects, activities and events surrounding them. Rec
Externí odkaz:
http://arxiv.org/abs/1912.10132
With the recent advancements in Artificial Intelligence (AI), Intelligent Virtual Assistants (IVA) such as Alexa, Google Home, etc., have become a ubiquitous part of many homes. Currently, such IVAs are mostly audio-based, but going forward, we are w
Externí odkaz:
http://arxiv.org/abs/1912.10131
Publikováno v:
Proceedings of the 23rd Workshop on the Semantics and Pragmatics of Dialogue (SEMDIAL), pp. 146-155, London, United Kingdom, September 2019
Building a machine learning driven spoken dialog system for goal-oriented interactions involves careful design of intents and data collection along with development of intent recognition models and dialog policy learning algorithms. The models should
Externí odkaz:
http://arxiv.org/abs/1912.10130