44 citations · 244 across the 30 of their papers we have counts for
6 papers · 1 filter
Deep Reinforcement Learning with Task-Adaptive Retrieval via Hypernetwork
Yonggang Jin, Chenxu Wang, Tianyu Zheng +5
Deep reinforcement learning algorithms are usually impeded by sampling inefficiency, heavily depending on multiple interactions with the environment to acquire accurate decision-ma…
HUB: Guiding Learned Optimizers with Continuous Prompt Tuning
Gaole Dai, Wei Wu, Ziyu Wang +3
Learned optimizers are a crucial component of meta-learning. Recent advancements in scalable learned optimizers have demonstrated their superior performance over hand-designed opti…
Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models
Shuai Zhao, Jinming Wen, Luu Anh Tuan +2
The prompt-based learning paradigm, which bridges the gap between pre-training and fine-tuning, achieves state-of-the-art performance on several NLP tasks, particularly in few-shot…
Chinese Open Instruction Generalist: A Preliminary Release
Ge Zhang, Yemin Shi, Ruibo Liu +9
Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release…
Modular Retrieval for Generalization and Interpretation
Juhao Liang, Chen Zhang, Zhengyang Tang +3
New retrieval tasks have always been emerging, thus urging the development of new retrieval models. However, instantiating a retrieval model for each new retrieval task is resource…
A Pathway Towards Responsible AI Generated Content
Chen Chen, Jie Fu, Lingjuan Lyu
AI Generated Content (AIGC) has received tremendous attention within the past few years, with content generated in the format of image, text, audio, video, etc. Meanwhile, AIGC has…