454 citations · 3.1k across the 117 of their papers we have counts for
118 papers
Encoding Context into LLM Weights: What Makes It Context?
Yue Wang, Qizhou Wang, Feng Liu +3
Longer contexts allow large language models (LLMs) to condition their predictions on more information, but inference costs grow prohibitively with context length. An emerging alter…
BrokenBind: Universal Modality Exploration beyond Dataset Boundaries
Zhuo Huang, Runnan Chen, Bo Han +3
Multi-modal learning combines various modalities to provide a comprehensive understanding of real-world problems. A common strategy is to directly bind different modalities togethe…
When and Why Does Multi-Agent Debate Fail and Does It Really Underperform?
Yongqiang Chen, Gang Niu, James Cheng +2
Multi-agent debate (MAD) was proposed as a promising approach for ensembling the wisdom of multiple large language models (LLMs) to improve reasoning and provide effective supervis…
Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers
Xin-Qiang Cai, Wei Wang, Feng Liu +3
Reinforcement Learning with Verifiable Rewards (RLVR) replaces costly human labeling with automated verifiers. To reduce verifier hacking, many RLVR systems binarize rewards to $\{…
On the Role of Label Noise in the Feature Learning Process
Andi Han, Wei Huang, Zhanpeng Zhou +5
Deep learning with noisy labels presents significant challenges. In this work, we theoretically characterize the role of label noise from a feature learning perspective. Specifical…
Accurate Forgetting for Heterogeneous Federated Continual Learning
Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang +6
Recent years have witnessed a burgeoning interest in federated learning (FL). However, the contexts in which clients engage in sequential learning remain under-explored. Bridging F…