5 papers
Agent Q-Mix: Selecting the Right Action for LLM Multi-Agent Systems through Reinforcement Learning
Eric Hanchen Jiang, Levina Li, Rui Sun +9
Large Language Models (LLMs) have shown remarkable performance in completing various tasks. However, solving complex problems often requires the coordination of multiple agents, ra…
BEDA: Belief Estimation as Probabilistic Constraints for Performing Strategic Dialogue Acts
Hengli Li, Zhaoxin Yu, Qi Shen +8
Strategic dialogue requires agents to execute distinct dialogue acts, for which belief estimation is essential. While prior work often estimates beliefs accurately, it lacks a prin…
Discrete Markov Bridge
Hengli Li, Yuxuan Wang, Song-Chun Zhu +2
Discrete diffusion has recently emerged as a promising paradigm in discrete data modeling. However, existing methods typically rely on a fixed rate transition matrix during trainin…
Are the Values of LLMs Structurally Aligned with Humans? A Causal Perspective
Yipeng Kang, Junqi Wang, Yexin Li +8
As large language models (LLMs) become increasingly integrated into critical applications, aligning their behavior with human values presents significant challenges. Current method…
How to Synthesize Text Data without Model Collapse?
Xuekai Zhu, Daixuan Cheng, Hengli Li +7
Model collapse in synthetic data indicates that iterative training on self-generated data leads to a gradual decline in performance. With the proliferation of AI models, synthetic…