Papers by Daoxin Zhang
Pause or Fabricate? Training Language Models for Grounded Reasoning (2026.findings-acl)
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Yiwen Qiu, Linjuan Wu, Yizhou Liu, Yuchen Yan, Jin Ma, Xu Tan, Yao Hu, Daoxin Zhang, Wenqi Zhang, Weiming Lu, Jun Xiao, Yongliang Shen
| Challenge: | Large language models implicitly fabricate information when inputs are incomplete, causing confidence but unreliable conclusions. |
| Approach: | They propose a framework for grounded reasoning under incomplete information that decomposes reasoning into two stages . they propose stage-specific rewards to penalize hallucinations, enabling models to detect gaps, stop proactively, and resume reasoning after clarification. |
| Outcome: | The proposed framework improves premise detection and task success by 30% . it also reduces average response length by over 20% . |
Leveraging Outline-Optimized Generative Interactions and Critique for Self-Refining Outlines with Reinforcement Learning (2026.acl-long)
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| Challenge: | Logic-RL is a framework that transforms critique-guided outline refinement into a learnable policy through reinforcement learning. |
| Approach: | They propose a framework that transforms critique-guided outline refinement into a learnable policy through reinforcement learning. |
| Outcome: | The proposed framework improves on FreshWiki and WikiOutline . it can be iteratively applied, with improved quality continuing through three refinement rounds before diminishing returns. |
Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases (2022.naacl-main)
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| Challenge: | Recursive noun phrases have interesting semantic properties, yet it is unknown whether language models have such knowledge. |
| Approach: | They propose a dataset of three textual inference tasks targeting recursive noun phrases . they show that such knowledge is learnable with appropriate data . |
| Outcome: | The proposed model achieves strong zero-shot performance on an extrinsic Harm Detection task. |