Papers by Yongliang Wu
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% . |
ToM-Synth: Scaling Robust Theory of Mind in LLMs via 6,912 Structured Social Units (2026.findings-acl)
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Guiyang Hou, Xiang Huang, Shangke Lyu, Yuchuan Wu, Weiyao Luo, Xinyu Mei, Yongliang Shen, Weiming Lu, Yongbin Li
| Challenge: | Existing methods endowing LLMs with Theory of Mind fail to internalize the augmented ToM into the LLM. |
| Approach: | They propose a factorial combinatorial synthesis framework that enables systematic synthesis of ToM data and uses it for RL fine-tuning. |
| Outcome: | The proposed framework yields a training dataset of 27,648 instances. |
Logic: Long-form Outline Generation via Imitative and Critical Self-refinement (2025.findings-emnlp)
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| Challenge: | Existing methods for long-form outline generation have low knowledge density and lack detail . retrieval-augmented approaches struggle to maintain logical coherence across retrieved information . |
| Approach: | They propose a system that mimics human writers' refinement process by mimicking outlines through imitation and critical self-refinement. |
| Outcome: | The proposed system improves on the FreshWiki and WikiOutline datasets and establishes a coherent planning framework and structured knowledge base. |
Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization (2024.acl-long)
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Wenqi Zhang, Ke Tang, Hai Wu, Mengna Wang, Yongliang Shen, Guiyang Hou, Zeqi Tan, Peng Li, Yueting Zhuang, Weiming Lu
| Challenge: | Large Language Models (LLMs) are designed as specific task solvers with sophisticated prompt engineering, but are inherently incapacitating to address complex dynamic scenarios. |
| Approach: | They propose an LLM-based agent with policy-level reflection and optimization that can learn from interactive experiences and progressively elevate its behavioral policy. |
| Outcome: | The proposed agent outperforms vanilla LLM and specialized models in blackjack and Texas hold’em. |
AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification (2025.emnlp-main)
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Xuan Zhang, Yongliang Shen, Zhe Zheng, Linjuan Wu, Wenqi Zhang, Yuchen Yan, Qiuying Peng, Jun Wang, Weiming Lu
| Challenge: | Existing tools for ambiguous and incomplete queries are limited by manual construction and lack of error correction mechanisms during multi-turn clarification. |
| Approach: | They propose a framework that exploits the mapping between queries and their tool invocation solutions by removing key parameters from queries while retaining them as ground truth. |
| Outcome: | The proposed framework outperforms existing methods while maintaining high accuracy in tool invocation. |
MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition (2023.emnlp-main)
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| Challenge: | Distantly supervised named entity recognition (DS-NER) aims to locate entity mentions and classify their types with knowledge bases or gazetteers and unlabeled corpus. |
| Approach: | They propose a noise-robust prototype network named MProto for a DS-NER task . they propose an optimal transport algorithm to mitigate the noise from incomplete labeling . |
| Outcome: | The proposed network achieves state-of-the-art on several DS-NER benchmarks. |
RSVP: Reasoning Segmentation via Visual Prompting and Multi-modal Chain-of-Thought (2025.acl-long)
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Yi Lu, Jiawang Cao, Yongliang Wu, Bozheng Li, Licheng Tang, Yangguang Ji, Chong Wu, Jay Wu, Wenbo Zhu
| Challenge: | Recent advances in multi-modal learning have enhanced MLLMs' ability to reason about visual content. |
| Approach: | They propose a framework that unifies multi-step multimodal reasoning with grounded visual understanding. |
| Outcome: | The proposed framework surpasses state-of-the-art methods by +6.5 gIoU and +9.2 cIou on ReasonSeg and achieves 49.7 mAP on SegInW under zero-shot settings. |
De-Bias for Generative Extraction in Unified NER Task (2022.acl-long)
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| Challenge: | Existing methods for Named entity recognition (NER) are not consistent with the task, which makes the model vulnerable to incorrect biases. |
| Approach: | They propose to use generative model to recognize entities from sentences . they analyze incorrect biases in the generation process from a causal perspective . |
| Outcome: | The proposed method improves the performance of the generative NER model in various datasets. |
PromptNER: Prompt Locating and Typing for Named Entity Recognition (2023.acl-long)
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Yongliang Shen, Zeqi Tan, Shuhui Wu, Wenqi Zhang, Rongsheng Zhang, Yadong Xi, Weiming Lu, Yueting Zhuang
| Challenge: | Existing methods for prompt learning require a multi-round prompting manner and require elaborate templates. |
| Approach: | They propose to unify entity locating and entity typing into prompt learning by enumerating spans to predict their entity types or constructing type-specific prompts to locate entities. |
| Outcome: | The proposed model outperforms the state-of-the-art model in a few-shot setting . it uses a template filled with multiple prompts and a bipartite graph matching mechanism . |
Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives (2024.acl-long)
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| Challenge: | Recent research indicates without external feedback, LLM’s intrinsic reflection is unstable. |
| Approach: | They propose a method that combines self-evaluated and external feedback to improve LLM's reflection. |
| Outcome: | The proposed method improves the quality of self-evaluated feedback and can catalyze more accurate and stable reflection. |
AutoTaskEval: Towards Domain-Specific and Fine-Grained Evaluation for LLMs (2026.acl-long)
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Qingqing Lyu, Linjuan Wu, Yongliang Shen, Hengwei Liu, Hao Li, Shengpei Jiang, Yin Zhang, Weiming Lu
| Challenge: | Existing automated approaches operate within fixed task schemas and often fail to autonomously discover new evaluation dimensions. |
| Approach: | They propose an automated framework that constructs domain-specific benchmarks directly from unstructured corpora using Bloom’s Taxonomy. |
| Outcome: | The proposed framework uncovers a broader and more fine-grained task space than expert-curated benchmarks while producing high-quality instances that preserve established model-level evaluation trends. |
TimeToM: Temporal Space is the Key to Unlocking the Door of Large Language Models’ Theory-of-Mind (2024.findings-acl)
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| Challenge: | Theory of Mind (ToM) is the foundation of social interaction and is crucial for social interaction. |
| Approach: | They propose a tool-belief solver that can transform a character’s higher-order beliefs into another character’ s first-order belief under belief communication period. |
| Outcome: | The proposed model improves the ToM capabilities of Large Language Models (LLMs) in multiple scenarios. |