Zobrazeno 1 - 10
of 197
pro vyhledávání: '"ZHAO Handong"'
Autor:
Nguyen, Dang, Lai, Viet Dac, Yoon, Seunghyun, Rossi, Ryan A., Zhao, Handong, Zhang, Ruiyi, Mathur, Puneet, Lipka, Nedim, Wang, Yu, Bui, Trung, Dernoncourt, Franck, Zhou, Tianyi
Existing LLM agent systems typically select actions from a fixed and predefined set at every step. While this approach is effective in closed, narrowly-scoped environments, we argue that it presents two major challenges when deploying LLM agents in r
Externí odkaz:
http://arxiv.org/abs/2411.01747
Autor:
Wu, Qiucheng, Zhao, Handong, Saxon, Michael, Bui, Trung, Wang, William Yang, Zhang, Yang, Chang, Shiyu
Vision language models (VLMs) are an exciting emerging class of language models (LMs) that have merged classic LM capabilities with those of image processing systems. However, the ways that these capabilities combine are not always intuitive and warr
Externí odkaz:
http://arxiv.org/abs/2407.01863
Despite recent advances in the general visual instruction-following ability of Multimodal Large Language Models (MLLMs), they still struggle with critical problems when required to provide a precise and detailed response to a visual instruction: (1)
Externí odkaz:
http://arxiv.org/abs/2406.10839
Autor:
Cao, Shengcao, Gu, Jiuxiang, Kuen, Jason, Tan, Hao, Zhang, Ruiyi, Zhao, Handong, Nenkova, Ani, Gui, Liang-Yan, Sun, Tong, Wang, Yu-Xiong
Open-world entity segmentation, as an emerging computer vision task, aims at segmenting entities in images without being restricted by pre-defined classes, offering impressive generalization capabilities on unseen images and concepts. Despite its pro
Externí odkaz:
http://arxiv.org/abs/2404.12386
Publikováno v:
Zhongguo Jianchuan Yanjiu, Vol 13, Iss 1, Pp 133-139 (2018)
[Objectives] This paper proposes a heterogeneous integrated learner to solve the problem of fuzzy uncertainty classification in order to judge the target intention of air attack in a short time. [Methods] First, a limit learning machine, decision tre
Externí odkaz:
https://doaj.org/article/4ec252a46488451b835dfe9bf6c5cba9
Autor:
Kim, Hyunjae, Yoon, Seunghyun, Bui, Trung, Zhao, Handong, Tran, Quan, Dernoncourt, Franck, Kang, Jaewoo
Contrastive language-image pre-training (CLIP) models have demonstrated considerable success across various vision-language tasks, such as text-to-image retrieval, where the model is required to effectively process natural language input to produce a
Externí odkaz:
http://arxiv.org/abs/2402.15120
Autor:
Liu, Yujian, Ji, Jiabao, Yu, Tong, Rossi, Ryan, Kim, Sungchul, Zhao, Handong, Sinha, Ritwik, Zhang, Yang, Chang, Shiyu
Table question answering is a popular task that assesses a model's ability to understand and interact with structured data. However, the given table often does not contain sufficient information for answering the question, necessitating the integrati
Externí odkaz:
http://arxiv.org/abs/2401.15555
Large pretrained multilingual language models (ML-LMs) have shown remarkable capabilities of zero-shot cross-lingual transfer, without direct cross-lingual supervision. While these results are promising, follow-up works found that, within the multili
Externí odkaz:
http://arxiv.org/abs/2401.05792
Autor:
Wu, Junda, Yu, Tong, Wang, Rui, Song, Zhao, Zhang, Ruiyi, Zhao, Handong, Lu, Chaochao, Li, Shuai, Henao, Ricardo
Soft prompt tuning achieves superior performances across a wide range of few-shot tasks. However, the performances of prompt tuning can be highly sensitive to the initialization of the prompts. We also empirically observe that conventional prompt tun
Externí odkaz:
http://arxiv.org/abs/2306.04933
Autor:
Xie, Kaige, Yu, Tong, Wang, Haoliang, Wu, Junda, Zhao, Handong, Zhang, Ruiyi, Mahadik, Kanak, Nenkova, Ani, Riedl, Mark
In real-world scenarios, labeled samples for dialogue summarization are usually limited (i.e., few-shot) due to high annotation costs for high-quality dialogue summaries. To efficiently learn from few-shot samples, previous works have utilized massiv
Externí odkaz:
http://arxiv.org/abs/2305.12077