Zobrazeno 1 - 10
of 675
pro vyhledávání: '"Ma Yubo"'
Autor:
Wu, Xiaobao, Pan, Liangming, Xie, Yuxi, Zhou, Ruiwen, Zhao, Shuai, Ma, Yubo, Du, Mingzhe, Mao, Rui, Luu, Anh Tuan, Wang, William Yang
Data contamination hinders fair LLM evaluation by introducing test data into newer models' training sets. Existing studies solve this challenge by updating benchmarks with newly collected data. However, they fail to guarantee contamination-free evalu
Externí odkaz:
http://arxiv.org/abs/2412.13670
This study presents a novel evaluation framework for the Vision-Language Navigation (VLN) task. It aims to diagnose current models for various instruction categories at a finer-grained level. The framework is structured around the context-free gramma
Externí odkaz:
http://arxiv.org/abs/2409.17313
Current Large Language Models (LLMs) exhibit limited ability to understand table structures and to apply precise numerical reasoning, which is crucial for tasks such as table question answering (TQA) and table-based fact verification (TFV). To addres
Externí odkaz:
http://arxiv.org/abs/2409.11724
Autor:
Duan, Haodong, Yang, Junming, Qiao, Yuxuan, Fang, Xinyu, Chen, Lin, Liu, Yuan, Agarwal, Amit, Chen, Zhe, Li, Mo, Ma, Yubo, Sun, Hailong, Zhao, Xiangyu, Cui, Junbo, Dong, Xiaoyi, Zang, Yuhang, Zhang, Pan, Wang, Jiaqi, Lin, Dahua, Chen, Kai
We present VLMEvalKit: an open-source toolkit for evaluating large multi-modality models based on PyTorch. The toolkit aims to provide a user-friendly and comprehensive framework for researchers and developers to evaluate existing multi-modality mode
Externí odkaz:
http://arxiv.org/abs/2407.11691
Autor:
Ma, Yubo, Zang, Yuhang, Chen, Liangyu, Chen, Meiqi, Jiao, Yizhu, Li, Xinze, Lu, Xinyuan, Liu, Ziyu, Ma, Yan, Dong, Xiaoyi, Zhang, Pan, Pan, Liangming, Jiang, Yu-Gang, Wang, Jiaqi, Cao, Yixin, Sun, Aixin
Understanding documents with rich layouts and multi-modal components is a long-standing and practical task. Recent Large Vision-Language Models (LVLMs) have made remarkable strides in various tasks, particularly in single-page document understanding
Externí odkaz:
http://arxiv.org/abs/2407.01523
Autor:
Ma, Yubo, Gou, Zhibin, Hao, Junheng, Xu, Ruochen, Wang, Shuohang, Pan, Liangming, Yang, Yujiu, Cao, Yixin, Sun, Aixin, Awadalla, Hany, Chen, Weizhu
Scientific reasoning poses an excessive challenge for even the most advanced Large Language Models (LLMs). To make this task more practical and solvable for LLMs, we introduce a new task setting named tool-augmented scientific reasoning. This setting
Externí odkaz:
http://arxiv.org/abs/2402.11451
Event relations are crucial for narrative understanding and reasoning. Governed by nuanced logic, event relation extraction (ERE) is a challenging task that demands thorough semantic understanding and rigorous logical reasoning. In this paper, we con
Externí odkaz:
http://arxiv.org/abs/2310.09158
Although achieving great success, Large Language Models (LLMs) usually suffer from unreliable hallucinations. Although language attribution can be a potential solution, there are no suitable benchmarks and evaluation metrics to attribute LLMs to stru
Externí odkaz:
http://arxiv.org/abs/2310.05634
Few-shot event detection (ED) has been widely studied, while this brings noticeable discrepancies, e.g., various motivations, tasks, and experimental settings, that hinder the understanding of models for future progress.This paper presents a thorough
Externí odkaz:
http://arxiv.org/abs/2305.01901
Publikováno v:
中国工程科学, Vol 26, Iss 4, Pp 28-39 (2024)
Energy security is a critical component of national security and a prerequisite for the green and low-carbon transition of the energy system. The concept of energy security has broadened from a traditional focus on fossil fuel supply to include the s
Externí odkaz:
https://doaj.org/article/518fba7c37e942e892089f9d63c4cd0b
