Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Do, Xuan Long"'
The proliferation of online toxic speech is a pertinent problem posing threats to demographic groups. While explicit toxic speech contains offensive lexical signals, implicit one consists of coded or indirect language. Therefore, it is crucial for mo
Externí odkaz:
http://arxiv.org/abs/2403.16685
Autor:
Zhao, Yiran, Zheng, Wenyue, Cai, Tianle, Do, Xuan Long, Kawaguchi, Kenji, Goyal, Anirudh, Shieh, Michael
Safety of Large Language Models (LLMs) has become a critical issue given their rapid progresses. Greedy Coordinate Gradient (GCG) is shown to be effective in constructing adversarial prompts to break the aligned LLMs, but optimization of GCG is time-
Externí odkaz:
http://arxiv.org/abs/2403.01251
Autor:
Do, Xuan Long, Hassanpour, Mohammad, Masry, Ahmed, Kavehzadeh, Parsa, Hoque, Enamul, Joty, Shafiq
A number of tasks have been proposed recently to facilitate easy access to charts such as chart QA and summarization. The dominant paradigm to solve these tasks has been to fine-tune a pretrained model on the task data. However, this approach is not
Externí odkaz:
http://arxiv.org/abs/2312.10610
Autor:
Do, Xuan Long, Zhao, Yiran, Brown, Hannah, Xie, Yuxi, Zhao, James Xu, Chen, Nancy F., Kawaguchi, Kenji, Shieh, Michael, He, Junxian
We propose a new method, Adversarial In-Context Learning (adv-ICL), to optimize prompt for in-context learning (ICL) by employing one LLM as a generator, another as a discriminator, and a third as a prompt modifier. As in traditional adversarial lear
Externí odkaz:
http://arxiv.org/abs/2312.02614
Mathematical questioning is crucial for assessing students problem-solving skills. Since manually creating such questions requires substantial effort, automatic methods have been explored. Existing state-of-the-art models rely on fine-tuning strategi
Externí odkaz:
http://arxiv.org/abs/2312.01661
Charts are very popular for analyzing data, visualizing key insights and answering complex reasoning questions about data. To facilitate chart-based data analysis using natural language, several downstream tasks have been introduced recently such as
Externí odkaz:
http://arxiv.org/abs/2305.14761
Autor:
Do, Xuan Long, Zou, Bowei, Joty, Shafiq, Tran, Anh Tai, Pan, Liangming, Chen, Nancy F., Aw, Ai Ti
Conversational Question Generation (CQG) is a critical task for machines to assist humans in fulfilling their information needs through conversations. The task is generally cast into two different settings: answer-aware and answer-unaware. While the
Externí odkaz:
http://arxiv.org/abs/2305.03088
Autor:
Zhao, Ruochen, Chen, Hailin, Wang, Weishi, Jiao, Fangkai, Do, Xuan Long, Qin, Chengwei, Ding, Bosheng, Guo, Xiaobao, Li, Minzhi, Li, Xingxuan, Joty, Shafiq
As Large Language Models (LLMs) become popular, there emerged an important trend of using multimodality to augment the LLMs' generation ability, which enables LLMs to better interact with the world. However, there lacks a unified perception of at whi
Externí odkaz:
http://arxiv.org/abs/2303.10868
Autor:
Khan, Mohammad Abdullah Matin, Bari, M Saiful, Do, Xuan Long, Wang, Weishi, Parvez, Md Rizwan, Joty, Shafiq
Recently, pre-trained large language models (LLMs) have shown impressive abilities in generating codes from natural language descriptions, repairing buggy codes, translating codes between languages, and retrieving relevant code segments. However, the
Externí odkaz:
http://arxiv.org/abs/2303.03004
Autor:
Kantharaj, Shankar, Do, Xuan Long, Leong, Rixie Tiffany Ko, Tan, Jia Qing, Hoque, Enamul, Joty, Shafiq
Charts are very popular to analyze data and convey important insights. People often analyze visualizations to answer open-ended questions that require explanatory answers. Answering such questions are often difficult and time-consuming as it requires
Externí odkaz:
http://arxiv.org/abs/2210.06628