Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Low, Bryan Kian Hsiang"'
Contextual dueling bandit is used to model the bandit problems, where a learner's goal is to find the best arm for a given context using observed noisy preference feedback over the selected arms for the past contexts. However, existing algorithms ass
Externí odkaz:
http://arxiv.org/abs/2407.17112
Large language models (LLMs) are widely used in decision-making, but their reliability, especially in critical tasks like healthcare, is not well-established. Therefore, understanding how LLMs reason and make decisions is crucial for their safe deplo
Externí odkaz:
http://arxiv.org/abs/2407.14845
The rapid evolution of large language models (LLMs) represents a substantial leap forward in natural language understanding and generation. However, alongside these advancements come significant challenges related to the accountability and transparen
Externí odkaz:
http://arxiv.org/abs/2407.04981
Autor:
Lau, Gregory Kang Ruey, Niu, Xinyuan, Dao, Hieu, Chen, Jiangwei, Foo, Chuan-Sheng, Low, Bryan Kian Hsiang
Protecting intellectual property (IP) of text such as articles and code is increasingly important, especially as sophisticated attacks become possible, such as paraphrasing by large language models (LLMs) or even unauthorized training of LLMs on copy
Externí odkaz:
http://arxiv.org/abs/2407.04411
Autor:
Xu, Xinyi, Wu, Zhaoxuan, Qiao, Rui, Verma, Arun, Shu, Yao, Wang, Jingtan, Niu, Xinyuan, He, Zhenfeng, Chen, Jiangwei, Zhou, Zijian, Lau, Gregory Kang Ruey, Dao, Hieu, Agussurja, Lucas, Sim, Rachael Hwee Ling, Lin, Xiaoqiang, Hu, Wenyang, Dai, Zhongxiang, Koh, Pang Wei, Low, Bryan Kian Hsiang
This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs). We start by making the key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential
Externí odkaz:
http://arxiv.org/abs/2406.14473
The increasing complexity of foundational models underscores the necessity for explainability, particularly for fine-tuning, the most widely used training method for adapting models to downstream tasks. Instance attribution, one type of explanation,
Externí odkaz:
http://arxiv.org/abs/2406.04606
Autor:
Lin, Xiaoqiang, Dai, Zhongxiang, Verma, Arun, Ng, See-Kiong, Jaillet, Patrick, Low, Bryan Kian Hsiang
Large language models (LLMs) have demonstrated remarkable performances in various tasks. However, the performance of LLMs heavily depends on the input prompt, which has given rise to a number of recent works on prompt optimization. However, previous
Externí odkaz:
http://arxiv.org/abs/2405.17346
Autor:
Wu, Zhaoxuan, Lin, Xiaoqiang, Dai, Zhongxiang, Hu, Wenyang, Shu, Yao, Ng, See-Kiong, Jaillet, Patrick, Low, Bryan Kian Hsiang
Large language models (LLMs) have shown impressive capabilities in real-world applications. The capability of in-context learning (ICL) allows us to adapt an LLM to downstream tasks by including input-label exemplars in the prompt without model fine-
Externí odkaz:
http://arxiv.org/abs/2405.16122
In-context learning (ICL) allows transformer-based language models that are pre-trained on general text to quickly learn a specific task with a few "task demonstrations" without updating their parameters, significantly boosting their flexibility and
Externí odkaz:
http://arxiv.org/abs/2405.14899
Physics-Informed Neural Networks (PINNs), which incorporate PDEs as soft constraints, train with a composite loss function that contains multiple training point types: different types of collocation points chosen during training to enforce each PDE a
Externí odkaz:
http://arxiv.org/abs/2404.07662