Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Si Chenglei"'
Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas. Despite th
Externí odkaz:
http://arxiv.org/abs/2409.04109
Autor:
Xiao, Chaojun, Zhang, Zhengyan, Song, Chenyang, Jiang, Dazhi, Yao, Feng, Han, Xu, Wang, Xiaozhi, Wang, Shuo, Huang, Yufei, Lin, Guanyu, Chen, Yingfa, Zhao, Weilin, Tu, Yuge, Zhong, Zexuan, Zhang, Ao, Si, Chenglei, Moo, Khai Hao, Zhao, Chenyang, Chen, Huimin, Lin, Yankai, Liu, Zhiyuan, Shang, Jingbo, Sun, Maosong
Advancements in LLMs have recently unveiled challenges tied to computational efficiency and continual scalability due to their requirements of huge parameters, making the applications and evolution of these models on devices with limited computation
Externí odkaz:
http://arxiv.org/abs/2409.02877
Autor:
Shen, Hua, Knearem, Tiffany, Ghosh, Reshmi, Alkiek, Kenan, Krishna, Kundan, Liu, Yachuan, Ma, Ziqiao, Petridis, Savvas, Peng, Yi-Hao, Qiwei, Li, Rakshit, Sushrita, Si, Chenglei, Xie, Yutong, Bigham, Jeffrey P., Bentley, Frank, Chai, Joyce, Lipton, Zachary, Mei, Qiaozhu, Mihalcea, Rada, Terry, Michael, Yang, Diyi, Morris, Meredith Ringel, Resnick, Paul, Jurgens, David
Recent advancements in general-purpose AI have highlighted the importance of guiding AI systems towards the intended goals, ethical principles, and values of individuals and groups, a concept broadly recognized as alignment. However, the lack of clar
Externí odkaz:
http://arxiv.org/abs/2406.09264
Autor:
Schulhoff, Sander, Ilie, Michael, Balepur, Nishant, Kahadze, Konstantine, Liu, Amanda, Si, Chenglei, Li, Yinheng, Gupta, Aayush, Han, HyoJung, Schulhoff, Sevien, Dulepet, Pranav Sandeep, Vidyadhara, Saurav, Ki, Dayeon, Agrawal, Sweta, Pham, Chau, Kroiz, Gerson, Li, Feileen, Tao, Hudson, Srivastava, Ashay, Da Costa, Hevander, Gupta, Saloni, Rogers, Megan L., Goncearenco, Inna, Sarli, Giuseppe, Galynker, Igor, Peskoff, Denis, Carpuat, Marine, White, Jules, Anadkat, Shyamal, Hoyle, Alexander, Resnik, Philip
Generative Artificial Intelligence (GenAI) systems are being increasingly deployed across all parts of industry and research settings. Developers and end users interact with these systems through the use of prompting or prompt engineering. While prom
Externí odkaz:
http://arxiv.org/abs/2406.06608
Autor:
Liu, Ruibo, Wei, Jerry, Liu, Fangyu, Si, Chenglei, Zhang, Yanzhe, Rao, Jinmeng, Zheng, Steven, Peng, Daiyi, Yang, Diyi, Zhou, Denny, Dai, Andrew M.
The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs. Synthetic data has emerged as a promising solution by generat
Externí odkaz:
http://arxiv.org/abs/2404.07503
Generative AI has made rapid advancements in recent years, achieving unprecedented capabilities in multimodal understanding and code generation. This can enable a new paradigm of front-end development in which multimodal large language models (MLLMs)
Externí odkaz:
http://arxiv.org/abs/2403.03163
Autor:
Schulhoff, Sander, Pinto, Jeremy, Khan, Anaum, Bouchard, Louis-François, Si, Chenglei, Anati, Svetlina, Tagliabue, Valen, Kost, Anson Liu, Carnahan, Christopher, Boyd-Graber, Jordan
Large Language Models (LLMs) are deployed in interactive contexts with direct user engagement, such as chatbots and writing assistants. These deployments are vulnerable to prompt injection and jailbreaking (collectively, prompt hacking), in which mod
Externí odkaz:
http://arxiv.org/abs/2311.16119
Autor:
Si, Chenglei, Goyal, Navita, Wu, Sherry Tongshuang, Zhao, Chen, Feng, Shi, Daumé III, Hal, Boyd-Graber, Jordan
Large Language Models (LLMs) are increasingly used for accessing information on the web. Their truthfulness and factuality are thus of great interest. To help users make the right decisions about the information they get, LLMs should not only provide
Externí odkaz:
http://arxiv.org/abs/2310.12558
While recent large language models (LLMs) improve on various question answering (QA) datasets, it remains difficult for a single model to generalize across question types that require distinct reasoning abilities. We provide empirical evidence that s
Externí odkaz:
http://arxiv.org/abs/2305.14628
In-context learning (ICL) is an important paradigm for adapting large language models (LLMs) to new tasks, but the generalization behavior of ICL remains poorly understood. We investigate the inductive biases of ICL from the perspective of feature bi
Externí odkaz:
http://arxiv.org/abs/2305.13299