no code implementations • 23 Feb 2024 • Chen-Chen Zong, Ye-Wen Wang, Kun-Peng Ning, Haibo Ye, Sheng-Jun Huang
In this paper, we attempt to query examples that are both likely from known classes and highly informative, and propose a \textit{Bidirectional Uncertainty-based Active Learning} (BUAL) framework.
1 code implementation • 2 Feb 2024 • Kun-Peng Ning, Shuo Yang, Yu-Yang Liu, Jia-Yu Yao, Zhen-Hui Liu, Yu Wang, Ming Pang, Li Yuan
Existing large language models (LLMs) evaluation methods typically focus on testing the performance on some closed-environment and domain-specific benchmarks with human annotations.
1 code implementation • 2 Oct 2023 • Jia-Yu Yao, Kun-Peng Ning, Zhen-Hui Liu, Mu-Nan Ning, Li Yuan
This phenomenon forces us to revisit that hallucination may be another view of adversarial examples, and it shares similar features with conventional adversarial examples as the basic feature of LLMs.
no code implementations • 2 Aug 2023 • Kun-Peng Ning, Ming Pang, Zheng Fang, Xue Jiang, Xi-Wei Zhao, Chang-Ping Peng, Zhan-Gang Lin, Jing-He Hu, Jing-Ping Shao
To overcome this challenge, in this paper, we propose knowledge condensation (KC), a simple yet effective knowledge distillation framework to boost the classification performance of the online FastText model under strict low latency constraints.
1 code implementation • CVPR 2022 • Kun-Peng Ning, Xun Zhao, Yu Li, Sheng-Jun Huang
To tackle this open-set annotation (OSA) problem, we propose a new active learning framework called LfOSA, which boosts the classification performance with an effective sampling strategy to precisely detect examples from known classes for annotation.
no code implementations • 27 Mar 2021 • Kun-Peng Ning, Lue Tao, Songcan Chen, Sheng-Jun Huang
Recently, much research has been devoted to improving the model robustness by training with noise perturbations.
no code implementations • 27 Mar 2021 • Kun-Peng Ning, Hu Xu, Kun Zhu, Sheng-Jun Huang
Imitation learning is a primary approach to improve the efficiency of reinforcement learning by exploiting the expert demonstrations.
no code implementations • 14 Jun 2020 • Kun-Peng Ning, Sheng-Jun Huang
In this paper, we propose a novel framework to adaptively learn the policy by jointly interacting with the environment and exploiting the expert demonstrations.