Zobrazeno 1 - 10
of 96
pro vyhledávání: '"Çelikyılmaz, Aslı"'
Autor:
Bordes, Florian, Pang, Richard Yuanzhe, Ajay, Anurag, Li, Alexander C., Bardes, Adrien, Petryk, Suzanne, Mañas, Oscar, Lin, Zhiqiu, Mahmoud, Anas, Jayaraman, Bargav, Ibrahim, Mark, Hall, Melissa, Xiong, Yunyang, Lebensold, Jonathan, Ross, Candace, Jayakumar, Srihari, Guo, Chuan, Bouchacourt, Diane, Al-Tahan, Haider, Padthe, Karthik, Sharma, Vasu, Xu, Hu, Tan, Xiaoqing Ellen, Richards, Megan, Lavoie, Samuel, Astolfi, Pietro, Hemmat, Reyhane Askari, Chen, Jun, Tirumala, Kushal, Assouel, Rim, Moayeri, Mazda, Talattof, Arjang, Chaudhuri, Kamalika, Liu, Zechun, Chen, Xilun, Garrido, Quentin, Ullrich, Karen, Agrawal, Aishwarya, Saenko, Kate, Celikyilmaz, Asli, Chandra, Vikas
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guide us through unfamiliar environments to generative models that produce
Externí odkaz:
http://arxiv.org/abs/2405.17247
Autor:
Tuan, Yi-Lin, Chen, Xilun, Smith, Eric Michael, Martin, Louis, Batra, Soumya, Celikyilmaz, Asli, Wang, William Yang, Bikel, Daniel M.
As large language models (LLMs) become easily accessible nowadays, the trade-off between safety and helpfulness can significantly impact user experience. A model that prioritizes safety will cause users to feel less engaged and assisted while priorit
Externí odkaz:
http://arxiv.org/abs/2404.01295
Autor:
Gao, Silin, Dwivedi-Yu, Jane, Yu, Ping, Tan, Xiaoqing Ellen, Pasunuru, Ramakanth, Golovneva, Olga, Sinha, Koustuv, Celikyilmaz, Asli, Bosselut, Antoine, Wang, Tianlu
To achieve faithful reasoning that aligns with human expectations, large language models (LLMs) need to ground their reasoning to real-world knowledge (e.g., web facts, math and physical rules). Tools help LLMs access this external knowledge, but the
Externí odkaz:
http://arxiv.org/abs/2401.17464
Autor:
Golovneva, Olga, O'Brien, Sean, Pasunuru, Ramakanth, Wang, Tianlu, Zettlemoyer, Luke, Fazel-Zarandi, Maryam, Celikyilmaz, Asli
With recent advancements in large language models, methods like chain-of-thought prompting to elicit reasoning chains have been shown to improve results on reasoning tasks. However, tasks that require multiple steps of reasoning still pose significan
Externí odkaz:
http://arxiv.org/abs/2312.05180
Autor:
Shridhar, Kumar, Sinha, Koustuv, Cohen, Andrew, Wang, Tianlu, Yu, Ping, Pasunuru, Ram, Sachan, Mrinmaya, Weston, Jason, Celikyilmaz, Asli
In recent years, Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations? A popular concept, referred to as self-refinement, postulates that LLMs can detect and correct t
Externí odkaz:
http://arxiv.org/abs/2311.07961
Large Language Models (LLMs) are frequently used for multi-faceted language generation and evaluation tasks that involve satisfying intricate user constraints or taking into account multiple aspects and criteria. However, their performance can fall s
Externí odkaz:
http://arxiv.org/abs/2310.15123
Publikováno v:
EMNLP 2023 (findings)
Continual learning (CL) has two main objectives: preventing catastrophic forgetting (CF) and encouraging knowledge transfer (KT). The existing literature mainly focused on overcoming CF. Some work has also been done on KT when the tasks are similar.
Externí odkaz:
http://arxiv.org/abs/2310.09436
Large language models (LLMs) have advanced in large strides due to the effectiveness of the self-attention mechanism that processes and compares all tokens at once. However, this mechanism comes with a fundamental issue -- the predetermined context w
Externí odkaz:
http://arxiv.org/abs/2310.05029
Autor:
Jiang, Song, Shakeri, Zahra, Chan, Aaron, Sanjabi, Maziar, Firooz, Hamed, Xia, Yinglong, Akyildiz, Bugra, Sun, Yizhou, Li, Jinchao, Wang, Qifan, Celikyilmaz, Asli
Chain-of-thought (CoT) prompting, which offers step-by-step problem-solving rationales, has impressively unlocked the reasoning potential of large language models (LLMs). Yet, the standard CoT is less effective in problems demanding multiple reasonin
Externí odkaz:
http://arxiv.org/abs/2310.04743
Extensive work has shown that the performance and interpretability of commonsense reasoning can be improved via knowledge-augmented reasoning methods, where the knowledge that underpins the reasoning process is explicitly verbalized and utilized. How
Externí odkaz:
http://arxiv.org/abs/2310.04921