12 citations · 20 across the 7 of their papers we have counts for
9 papers
When Debate Helps: Proposal Supply and Verification-Aware Readout in Multi-Agent Reasoning
Zihao Zhao, Tunyu Zhang, Haizhou Shi +3
Multi-agent debate can improve reasoning, yet often fails to beat simple majority voting. We argue that successful debate requires two distinct mechanisms: proposal supply must sur…
Reward Modeling for Multi-Agent Orchestration
King Yeung Tsang, Zihao Zhao, Vishal Venkataramani +5
Multi-Agent Systems (MAS) built on Large Language Models (LLMs) require effective orchestration to coordinate specialized agents, yet training such orchestrators is hindered by lim…
SSR: Socratic Self-Refine for Large Language Model Reasoning
Haizhou Shi, Ye Liu, Bo Pang +6
Large Language Models (LLMs) have demonstrated remarkable reasoning abilities, yet existing test-time frameworks often rely on coarse self-verification and self-correction, limitin…
TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning
Tunyu Zhang, Haizhou Shi, Yibin Wang +9
While Large Language Models (LLMs) have demonstrated impressive capabilities, their output quality remains inconsistent across various application scenarios, making it difficult to…
Dist2ill: Distributional Distillation for One-Pass Uncertainty Estimation in Large Language Models
Yicong Zhao, King Yeung Tsang, Harshil Vejendla +9
Large Language Models (LLMs) often exhibit misalignment between the quality of their generated responses and the confidence estimates they assign to them. Bayesian treatments, such…
Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
Haizhou Shi, Yibin Wang, Ligong Han +2
Estimating the uncertainty of responses from Large Language Models (LLMs) remains a critical challenge. While recent Bayesian methods have demonstrated effectiveness in quantifying…
BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models
Yibin Wang, Haizhou Shi, Ligong Han +2
Large Language Models (LLMs) often suffer from overconfidence during inference, particularly when adapted to downstream domain-specific tasks with limited data. Previous work addre…
Continual Learning of Large Language Models: A Comprehensive Survey
Haizhou Shi, Zihao Xu, Hengyi Wang +6
The recent success of large language models (LLMs) trained on static, pre-collected, general datasets has sparked numerous research directions and applications. One such direction…
A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm
Haizhou Shi, Hao Wang
Domain incremental learning aims to adapt to a sequence of domains with access to only a small subset of data (i.e., memory) from previous domains. Various methods have been propos…