18 citations · 32 across the 21 of their papers we have counts for
10 papers · 1 filter
Constrained Language Model Policy Optimization via Risk-aware Stepwise Alignment
Lijun Zhang, Lin Li, Wei Wei +5
When fine-tuning pre-trained Language Models (LMs) to exhibit desired behaviors, maintaining control over risk is critical for ensuring both safety and trustworthiness. Most existi…
Consistency-Aware Editing for Entity-level Unlearning in Language Models
Xiaoqi Han, Víctor Gutiérrez-Basulto, Ru Li +3
Large language models (LLMs) risk retaining sensitive, copyrighted, or harmful information from their training data. Entity-level unlearning addresses this issue by removing all kn…
Learnable Game-theoretic Policy Optimization for Data-centric Self-explanation Rationalization
Yunxiao Zhao, Zhiqiang Wang, Xingtong Yu +3
Rationalization, a data-centric framework, aims to build self-explanatory models to explain the prediction outcome by generating a subset of human-intelligible pieces of the input…
Explaining Black-box Language Models with Knowledge Probing Systems: A Post-hoc Explanation Perspective
Yunxiao Zhao, Hao Xu, Zhiqiang Wang +3
Pre-trained Language Models (PLMs) are trained on large amounts of unlabeled data, yet they exhibit remarkable reasoning skills. However, the trustworthiness challenges posed by th…
Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies
Yi Ma, Hongyao Tang, Chenjun Xiao +4
In recent years, the expansion of neural network models and training data has driven remarkable progress in deep learning, particularly in computer vision and natural language proc…
Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities
Zhichao Yan, Jiapu Wang, Jiaoyan Chen +6
Retrieval-Augmented Generation (RAG) shows impressive performance by supplementing and substituting parametric knowledge in Large Language Models (LLMs). Retrieved knowledge can be…
Positive region preserved random sampling: an efficient feature selection method for massive data
Hexiang Bai, Deyu Li, Jiye Liang +1
Selecting relevant features is an important and necessary step for intelligent machines to maximize their chances of success. However, intelligent machines generally have no enough…
A Survey of Continual Reinforcement Learning
Chaofan Pan, Xin Yang, Yanhua Li +4
Reinforcement Learning (RL) is an important machine learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in this field…
C-LoRA: Continual Low-Rank Adaptation for Pre-trained Visual Models
Xin Zhang, Liang Bai, Xian Yang +1
Pre-trained visual models have become fundamental in computer vision, but they face challenges in continual learning scenarios where data and tasks evolve over time. Low-Rank Adapt…
Progressive Local Alignment for Medical Multimodal Pre-training
Huimin Yan, Xian Yang, Liang Bai +1
Local alignment between medical images and text is essential for accurate diagnosis, though it remains challenging due to the absence of natural local pairings and the limitations…