Education
CogVLM: Visual Expert for Pretrained Language Models
We introduce CogVLM, a powerful open-source visual language foundation model.
RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs Yue Y u Georgia Tech Wei Ping
Wang et al., 2024) is a widely used technique for customizing large language models (LLMs) to handle
Provably Efficient Interaction-Grounded Learning with Personalized Reward
Interaction-Grounded Learning (IGL) [Xie et al., 2021] is a powerful framework in To deal with personalized rewards that are ubiquitous in applications such as recommendation systems, Maghakian et al. [2022] study a version of IGL with context-dependent feedback, but their algorithm does not come with theoretical guarantees. Building on this estimator, we propose two algorithms, one based on explore-then-exploit and the other based on inverse-gap weighting.
An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling Shenbao Y u
By exploiting students' response logs, SCM aims to predict their exercise
Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning Wenke Huang 1, Mang Y e
Existing backdoor defense methods typically require either homogeneous assumption, validation datasets, or client optimization conflicts.
A Synthetic Dataset for Personal Attribute Inference Hanna Y ukhymenko
Recently powerful Large Language Models (LLMs) have become easily accessible to hundreds of millions of users world-wide.
Imitation learning (IL) aims to mimic the behavior of an expert in a sequential decision making task by learning from demonstrations, and has been widely applied
Full experimental details are provided in Appendix C.
Unleashing Region Understanding in Intermediate Layers for MLLM-based Referring Expression Generation
On the other hand, complicated sentences could easily increase the probability of hallucinations.
Can Large Language Models Explore In-Context?
We focus on native performance of existing LLMs, without training interventions.
Label Delay in Online Continual Learning
We introduce a new continual learning framework with explicit modeling of the label delay between data and label streams over time steps.