7 papers
DeliChess: A Multi-party Dialogue Dataset for Deliberation in Chess Puzzle Solving
Xiaochen Zhu, Georgi Karadzhov, Tom Stafford +1
Multi-party dialogue is a critical setting for studying collaborative reasoning and decision-making, yet existing datasets rarely focus on structured, reasoning-intensive tasks. We…
Demystifying Multi-Agent Debate: The Role of Confidence and Diversity
Xiaochen Zhu, Caiqi Zhang, Yizhou Chi +3
Multi-agent debate (MAD) is widely used to improve large language model (LLM) performance through test-time scaling, yet recent work shows that vanilla MAD often underperforms simp…
Confidence Estimation for LLMs in Multi-turn Interactions
Caiqi Zhang, Ruihan Yang, Xiaochen Zhu +5
While confidence estimation is a promising direction for mitigating hallucinations in Large Language Models (LLMs), current research overwhelmingly focuses on single-turn settings.…
LoVeC: Reinforcement Learning for Better Verbalized Confidence in Long-Form Generations
Caiqi Zhang, Xiaochen Zhu, Chengzu Li +2
Hallucination remains a major challenge for the safe and trustworthy deployment of large language models (LLMs) in factual content generation. Prior work has explored confidence es…
Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems
Jooyoung Lee, Xiaochen Zhu, Georgi Karadzhov +3
The proliferation of generative models has presented significant challenges in distinguishing authentic human-authored content from deepfake content. Collaborative human efforts, a…
Segment-Level Diffusion: A Framework for Controllable Long-Form Generation with Diffusion Language Models
Xiaochen Zhu, Georgi Karadzhov, Chenxi Whitehouse +1
Diffusion models have shown promise in text generation, but often struggle with generating long, coherent, and contextually accurate text. Token-level diffusion doesn't model word-…