Papers by Xiaochen Zhu
Conformity in Large Language Models (2025.acl-long)
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| Challenge: | Conformity is a form of social influence that affects the way people respond to information. |
| Approach: | They adapt psychological experiments to examine the extent of conformity in large language models. |
| Outcome: | The proposed interventions mitigate conformity by reducing the naturalness of majority tones and reducing instruction-tuned models. |
Demystifying Multi-Agent Debate: The Role of Confidence and Diversity (2026.findings-acl)
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| Challenge: | Multi-agent debate (MAD) is widely used to improve large language models' (LLMs) reasoning and test-time scaling. |
| Approach: | They propose a diversity-aware initialisation that selects a more diverse pool of candidate answers, increasing the likelihood that a correct hypothesis is present at the start of debate. |
| Outcome: | The proposed protocol outperforms vanilla MAD and majority vote on six reasoning-oriented QA benchmarks. |
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format (2023.emnlp-demo)
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Qi Zhu, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Baolin Peng, Zheng Zhang, Shutong Feng, Michael Heck, Nurul Lubis, Dazhen Wan, Xiaochen Zhu, Jianfeng Gao, Milica Gasic, Minlie Huang
| Challenge: | Existing tools for building TOD systems often lack a user-friendly interface . a toolkit with advanced, easily integrable modules is needed to bridge this gap . |
| Approach: | They propose a multifaceted dialogue system toolkit that integrates diverse datasets and models with a streamlined training process and in-depth evaluation tools. |
| Outcome: | The proposed toolkit combines RL and transfer learning to support the rapid development and evaluation of robust dialogue policies. |
Segment-Level Diffusion: A Framework for Controllable Long-Form Generation with Diffusion Language Models (2025.acl-long)
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| Challenge: | Diffusion models have shown promise in text generation, but often struggle with generating long, coherent, and contextually accurate text. |
| Approach: | They propose a framework that enhances diffusion-based text generation through text segmentation, robust representation training with adversarial and contrastive learning, and improved latent-space guidance. |
| Outcome: | The proposed framework improves diffusion-based text generation and improves scalability and fluency. |
LoVeC: Reinforcement Learning for Better Verbalized Confidence in Long-Form Generation (2026.acl-long)
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| Challenge: | Existing methods for hallucination detection are limited to short-form question answering tasks and do not generalize well to open-ended generation. |
| Approach: | They propose a method that trains LLMs to append a numerical confidence score to each generated statement during long-form generation. |
| Outcome: | The proposed method is 20 faster than traditional self-consistency methods while achieving better calibration. |
Confidence Estimation for LLMs in Multi-turn Interactions (2026.findings-acl)
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Caiqi Zhang, Ruihan Yang, Xiaochen Zhu, Chengzu Li, Tiancheng Hu, Yijiang River Dong, Deqing Yang, Nigel Collier
| Challenge: | Despite recent progress, most prior work studies confidence in single-turn question answering. |
| Approach: | They propose a logit-based probe that measures confidence in multi-turn dialogues . they propose 'infoECE' and a "hinter-guesser" paradigm for generating controlled evaluations based on data . |
| Outcome: | The proposed framework is grounded in calibration and monotonicity of confidence as more information becomes available. |