Zobrazeno 1 - 10
of 10 990
pro vyhledávání: '"Jieyu An"'
Autor:
Wang, Shijian, Song, Linxin, Zhang, Jieyu, Shimizu, Ryotaro, Luo, Ao, Yao, Li, Chen, Cunjian, McAuley, Julian, Wu, Hanqian
Current multimodal language models (MLMs) evaluation and training approaches overlook the influence of instruction format, presenting an elephant-in-the-room problem. Previous research deals with this problem by manually crafting instructions, failin
Externí odkaz:
http://arxiv.org/abs/2412.08307
DALL-E and Sora have gained attention by producing implausible images, such as "astronauts riding a horse in space." Despite the proliferation of text-to-vision models that have inundated the internet with synthetic visuals, from images to 3D assets,
Externí odkaz:
http://arxiv.org/abs/2412.08221
Autor:
Li, Lincan, Li, Jiaqi, Chen, Catherine, Gui, Fred, Yang, Hongjia, Yu, Chenxiao, Wang, Zhengguang, Cai, Jianing, Zhou, Junlong Aaron, Shen, Bolin, Qian, Alex, Chen, Weixin, Xue, Zhongkai, Sun, Lichao, He, Lifang, Chen, Hanjie, Ding, Kaize, Du, Zijian, Mu, Fangzhou, Pei, Jiaxin, Zhao, Jieyu, Swayamdipta, Swabha, Neiswanger, Willie, Wei, Hua, Hu, Xiyang, Zhu, Shixiang, Chen, Tianlong, Lu, Yingzhou, Shi, Yang, Qin, Lianhui, Fu, Tianfan, Tu, Zhengzhong, Yang, Yuzhe, Yoo, Jaemin, Zhang, Jiaheng, Rossi, Ryan, Zhan, Liang, Zhao, Liang, Ferrara, Emilio, Liu, Yan, Huang, Furong, Zhang, Xiangliang, Rothenberg, Lawrence, Ji, Shuiwang, Yu, Philip S., Zhao, Yue, Dong, Yushun
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and misinformation detection. Meanwhile, the need to systematically understan
Externí odkaz:
http://arxiv.org/abs/2412.06864
Autor:
Zhang, Jieyu, Xue, Le, Song, Linxin, Wang, Jun, Huang, Weikai, Shu, Manli, Yan, An, Ma, Zixian, Niebles, Juan Carlos, savarese, silvio, Xiong, Caiming, Chen, Zeyuan, Krishna, Ranjay, Xu, Ran
With the rise of multimodal applications, instruction data has become critical for training multimodal language models capable of understanding complex image-based queries. Existing practices rely on powerful but costly large language models (LLMs) o
Externí odkaz:
http://arxiv.org/abs/2412.07012
Autor:
Ma, Zixian, Zhang, Jianguo, Liu, Zhiwei, Zhang, Jieyu, Tan, Juntao, Shu, Manli, Niebles, Juan Carlos, Heinecke, Shelby, Wang, Huan, Xiong, Caiming, Krishna, Ranjay, Savarese, Silvio
While open-source multi-modal language models perform well on simple question answering tasks, they often fail on complex questions that require multiple capabilities, such as fine-grained recognition, visual grounding, and reasoning, and that demand
Externí odkaz:
http://arxiv.org/abs/2412.05479
Recent advancements in Large Language Models (LLMs) have opened new avenues for accelerating drug discovery processes. Despite their potential, several critical challenges remain unsolved, particularly in translating theoretical ideas into practical
Externí odkaz:
http://arxiv.org/abs/2411.15692
Autor:
Zhang, Shaokun, Zhang, Jieyu, Ding, Dujian, Garcia, Mirian Hipolito, Mallick, Ankur, Madrigal, Daniel, Xia, Menglin, Rühle, Victor, Wu, Qingyun, Wang, Chi
Recent advancements have enabled Large Language Models (LLMs) to function as agents that can perform actions using external tools. This requires registering, i.e., integrating tool information into the LLM context prior to taking actions. Current met
Externí odkaz:
http://arxiv.org/abs/2411.01643
Differential privacy (DP) is applied when fine-tuning pre-trained large language models (LLMs) to limit leakage of training examples. While most DP research has focused on improving a model's privacy-utility tradeoff, some find that DP can be unfair
Externí odkaz:
http://arxiv.org/abs/2410.18749
Designing functional transition metal complexes (TMCs) faces challenges due to the vast search space of metals and ligands, requiring efficient optimization strategies. Traditional genetic algorithms (GAs) are commonly used, employing random mutation
Externí odkaz:
http://arxiv.org/abs/2410.18136
Despite the remarkable success of Large Language Models (LLMs), evaluating their outputs' quality regarding preference remains a critical challenge. Existing works usually leverage a powerful LLM (e.g., GPT4) as the judge for comparing LLMs' output p
Externí odkaz:
http://arxiv.org/abs/2410.12869