Zobrazeno 1 - 10
of 239
pro vyhledávání: '"Yang, Yujiu"'
Autor:
Zhang, Yuxiang, Chen, Jing, Wang, Junjie, Liu, Yaxin, Yang, Cheng, Shi, Chufan, Zhu, Xinyu, Lin, Zihao, Wan, Hanwen, Yang, Yujiu, Sakai, Tetsuya, Feng, Tian, Yamana, Hayato
Tool-augmented large language models (LLMs) are rapidly being integrated into real-world applications. Due to the lack of benchmarks, the community still needs to fully understand the hallucination issues within these models. To address this challeng
Externí odkaz:
http://arxiv.org/abs/2406.20015
Autor:
Li, Haoling, Zhang, Xin, Liu, Xiao, Gong, Yeyun, Wang, Yifan, Yang, Yujiu, Chen, Qi, Cheng, Peng
Large language models (LLMs) have revolutionized lots of fields of research. Although it is well-known that fine-tuning is essential for enhancing the capabilities of LLMs, existing research suggests that there is potential redundancy in the fine-tun
Externí odkaz:
http://arxiv.org/abs/2406.15330
Autor:
Chen, Jing, Zhu, Xinyu, Yang, Cheng, Shi, Chufan, Xi, Yadong, Zhang, Yuxiang, Wang, Junjie, Pu, Jiashu, Zhang, Rongsheng, Yang, Yujiu, Feng, Tian
Generative AI has demonstrated unprecedented creativity in the field of computer vision, yet such phenomena have not been observed in natural language processing. In particular, large language models (LLMs) can hardly produce written works at the lev
Externí odkaz:
http://arxiv.org/abs/2406.11683
Autor:
Shi, Chufan, Yang, Cheng, Liu, Yaxin, Shui, Bo, Wang, Junjie, Jing, Mohan, Xu, Linran, Zhu, Xinyu, Li, Siheng, Zhang, Yuxiang, Liu, Gongye, Nie, Xiaomei, Cai, Deng, Yang, Yujiu
We introduce a new benchmark, ChartMimic, aimed at assessing the visually-grounded code generation capabilities of large multimodal models (LMMs). ChartMimic utilizes information-intensive visual charts and textual instructions as inputs, requiring L
Externí odkaz:
http://arxiv.org/abs/2406.09961
Autor:
Gu, Tianle, Zhou, Zeyang, Huang, Kexin, Liang, Dandan, Wang, Yixu, Zhao, Haiquan, Yao, Yuanqi, Qiao, Xingge, Wang, Keqing, Yang, Yujiu, Teng, Yan, Qiao, Yu, Wang, Yingchun
Powered by remarkable advancements in Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) demonstrate impressive capabilities in manifold tasks. However, the practical application scenarios of MLLMs are intricate, exposing them to
Externí odkaz:
http://arxiv.org/abs/2406.07594
With the availability of large pre-trained models, a modern workflow for building real-world machine learning solutions is to fine-tune such models on a downstream task with a relatively small domain-specific dataset. In such applications, one major
Externí odkaz:
http://arxiv.org/abs/2405.16027
The newly proposed Generalized Referring Expression Segmentation (GRES) amplifies the formulation of classic RES by involving multiple/non-target scenarios. Recent approaches focus on optimizing the last modality-fused feature which is directly utili
Externí odkaz:
http://arxiv.org/abs/2405.15658
Autor:
Shi, Chufan, Yang, Cheng, Zhu, Xinyu, Wang, Jiahao, Wu, Taiqiang, Li, Siheng, Cai, Deng, Yang, Yujiu, Meng, Yu
Mixture-of-Experts (MoE) has emerged as a prominent architecture for scaling model size while maintaining computational efficiency. In MoE, each token in the input sequence activates a different subset of experts determined by a routing mechanism. Ho
Externí odkaz:
http://arxiv.org/abs/2405.14507
Knowledge Graph Completion (KGC) has emerged as a promising solution to address the issue of incompleteness within Knowledge Graphs (KGs). Traditional KGC research primarily centers on triple classification and link prediction. Nevertheless, we conte
Externí odkaz:
http://arxiv.org/abs/2404.09897
Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts and use the concepts to make predictions, enhancing the transparency of the decision-making process. Mult
Externí odkaz:
http://arxiv.org/abs/2404.08978