Zobrazeno 1 - 10
of 59
pro vyhledávání: '"GUO Hangyu"'
Publikováno v:
Shipin yu jixie, Vol 40, Iss 4, Pp 220-226 (2024)
Because of high moisture content, blueberries are susceptible to disease and insect infestation leading to corruption and deterioration, which has serious influence on the shelf life of blueberry fruits. This article summarizes the cause mechanism of
Externí odkaz:
https://doaj.org/article/cb9ab44e0440493e83054e75514a0337
Autor:
Li, Shilong, He, Yancheng, Huang, Hui, Bu, Xingyuan, Liu, Jiaheng, Guo, Hangyu, Wang, Weixun, Gu, Jihao, Su, Wenbo, Zheng, Bo
Recent advancements in Direct Preference Optimization (DPO) have significantly enhanced the alignment of Large Language Models (LLMs) with human preferences, owing to its simplicity and effectiveness. However, existing methods typically optimize a sc
Externí odkaz:
http://arxiv.org/abs/2410.19720
Autor:
Wang, Pei, Wu, Yanan, Wang, Zekun, Liu, Jiaheng, Song, Xiaoshuai, Peng, Zhongyuan, Deng, Ken, Zhang, Chenchen, Wang, Jiakai, Peng, Junran, Zhang, Ge, Guo, Hangyu, Zhang, Zhaoxiang, Su, Wenbo, Zheng, Bo
Large Language Models (LLMs) have displayed massive improvements in reasoning and decision-making skills and can hold natural conversations with users. Recently, many tool-use benchmark datasets have been proposed. However, existing datasets have the
Externí odkaz:
http://arxiv.org/abs/2410.11710
As multimodal large language models (MLLMs) continue to demonstrate increasingly competitive performance across a broad spectrum of tasks, more intricate and comprehensive benchmarks have been developed to assess these cutting-edge models. These benc
Externí odkaz:
http://arxiv.org/abs/2410.06555
Autor:
Li, Yizhi, Zhang, Ge, Ma, Yinghao, Yuan, Ruibin, Zhu, Kang, Guo, Hangyu, Liang, Yiming, Liu, Jiaheng, Wang, Zekun, Yang, Jian, Wu, Siwei, Qu, Xingwei, Shi, Jinjie, Zhang, Xinyue, Yang, Zhenzhu, Wang, Xiangzhou, Zhang, Zhaoxiang, Liu, Zachary, Benetos, Emmanouil, Huang, Wenhao, Lin, Chenghua
Recent advancements in multimodal large language models (MLLMs) have aimed to integrate and interpret data across diverse modalities. However, the capacity of these models to concurrently process and reason about multiple modalities remains inadequat
Externí odkaz:
http://arxiv.org/abs/2409.15272
Autor:
Li, Shilong, He, Yancheng, Guo, Hangyu, Bu, Xingyuan, Bai, Ge, Liu, Jie, Liu, Jiaheng, Qu, Xingwei, Li, Yangguang, Ouyang, Wanli, Su, Wenbo, Zheng, Bo
Long-context capabilities are essential for large language models (LLMs) to tackle complex and long-input tasks. Despite numerous efforts made to optimize LLMs for long contexts, challenges persist in robustly processing long inputs. In this paper, w
Externí odkaz:
http://arxiv.org/abs/2406.14550
Large language models have seen widespread adoption in math problem-solving. However, in geometry problems that usually require visual aids for better understanding, even the most advanced multi-modal models currently still face challenges in effecti
Externí odkaz:
http://arxiv.org/abs/2406.11503
In this paper, we study the harmlessness alignment problem of multimodal large language models (MLLMs). We conduct a systematic empirical analysis of the harmlessness performance of representative MLLMs and reveal that the image input poses the align
Externí odkaz:
http://arxiv.org/abs/2403.09792
Autor:
Du, Yifan, Guo, Hangyu, Zhou, Kun, Zhao, Wayne Xin, Wang, Jinpeng, Wang, Chuyuan, Cai, Mingchen, Song, Ruihua, Wen, Ji-Rong
Visual instruction tuning is an essential approach to improving the zero-shot generalization capability of Multi-modal Large Language Models (MLLMs). A surge of visual instruction datasets with various focuses and characteristics have been proposed r
Externí odkaz:
http://arxiv.org/abs/2311.01487
Although pre-trained language models~(PLMs) have shown impressive performance by text-only self-supervised training, they are found lack of visual semantics or commonsense. Existing solutions often rely on explicit images for visual knowledge augment
Externí odkaz:
http://arxiv.org/abs/2212.07937