Papers by Yizhou Zhang
Tab-Cleaner: Weakly Supervised Tabular Data Cleaning via Pre-training for E-commerce Catalog (2023.acl-industry)
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Kewei Cheng, Xian Li, Zhengyang Wang, Chenwei Zhang, Binxuan Huang, Yifan Ethan Xu, Xin Luna Dong, Yizhou Sun
| Challenge: | Existing methods for analyzing textual attributes in product catalogs are not effective on structured tabular data since they are trained on free-form natural language texts. |
| Approach: | They propose a model to handle error detection over tabular data following a pre-training paradigm. |
| Outcome: | The proposed model improves on a real-world Amazon Product Catalog table by 16% over state-of-the-art methods and by 11% on PR AUC over attribute value validation task. |
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. |
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% . |
Revealing the Seen, Imagining the Beyond: A Survey of Image-Grounded Chain-of-Thought Reasoning in Multimodal LLMs (2026.acl-long)
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| Challenge: | Recent advances in Multimodal Large Language Models (MLLMs) have shifted visual reasoning from tool-calling to end-to-end perceptionreasoning. |
| Approach: | They synthesize the emerging paradigm of Image-Grounded Chain-of-Thought (IG-CoT) they propose a method-centric taxonomy covering prompting, supervised fine-tuning, and reinforcement learning . |
| Outcome: | The proposed model is based on a method-centric taxonomy and benchmarks. |
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs (2023.findings-acl)
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Zijie Huang, Daheng Wang, Binxuan Huang, Chenwei Zhang, Jingbo Shang, Yan Liang, Zhengyang Wang, Xian Li, Christos Faloutsos, Yizhou Sun, Wei Wang
| Challenge: | Existing methods to embed knowledge graphs have ignored the fact that they contain two fundamentally different views: high-level ontology-view concepts and fine-grained instance-view entities. |
| Approach: | They propose a novel geometric representation that jointly embeds the two views of a KG using dual geometric representations. |
| Outcome: | Experiments on the public DBpedia KG and a newly-created industrial KG show the proposed method works well. |
UniKER: A Unified Framework for Combining Embedding and Definite Horn Rule Reasoning for Knowledge Graph Inference (2021.emnlp-main)
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| Challenge: | Knowledge graph inference has been studied extensively due to its wide applications. |
| Approach: | They propose a framework that restricts logical rules to be definite Horn rules and can exploit the knowledge in logical rule-based reasoning and KGE in an extremely efficient way. |
| Outcome: | The proposed framework can exploit the knowledge in logical rules and improve KGE in an extremely efficient way. |
Data-Efficient Selection via Grammatical Complexity in Continual Pre-training of Domain-Specific LLMs (2025.emnlp-main)
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Yizhou Ying, Geng Zhang, Cui Danxin, Chengyu Du, Guanglei Yue, Sihang Jiang, Jiaqing Liang, Yifei Fu, Hailin Hu, Yanghua Xiao
| Challenge: | Existing data selection strategies for continual pre-training of large language models often rely on scarce labeled data or computationally expensive LLMs. |
| Approach: | They propose an annotation-independent data selection framework for CPT that evaluates grammatical complexity using lexical diversity and syntactic complexity. |
| Outcome: | The proposed framework outperforms baselines on a financial dataset and surpasses full-data training by 1.7% using only 20% of the data. |
HCSpec: Two-Tier Horizontal Cascade Speculative Decoding for High-Efficiency Large Language Model Inference (2026.acl-long)
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| Challenge: | Existing approaches to decode large language models adopt a homogeneous architecture . autoregressive decoding is a bottleneck because tokens must be generated sequentially . |
| Approach: | They propose a framework that organizes heterogeneous position-specialized draft modules into a horizontal cascade. |
| Outcome: | The proposed framework outperforms the current state-of-the-art (EAGLE3) and achieves 3.72x acceleration over vanilla decoding. |
From Words to Pixels: A Comprehensive Survey on Large Language Models in Visual Segmentation (2026.acl-long)
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| Challenge: | Visual segmentation with instruction has been a challenging task for many years . large language models and large multimodal models have spurred a new wave of research . |
| Approach: | They review recent works in LLM-based visual segmentation and analyze their architectural innovations, training strategies, and benchmark performance. |
| Outcome: | The present study reviews the most recent works in LLM-driven visual segmentation . it identifies key challenges and promising future directions . |
Cautious Next Token Prediction (2025.findings-acl)
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Yizhou Wang, Lingzhi Zhang, Yue Bai, Mang Tik Chiu, Zhengmian Hu, Mingyuan Zhang, Qihua Dong, Yu Yin, Sohrab Amirghodsi, Yun Fu
| Challenge: | Existing methods for decoding autoregressive models are temperature scaling and nucleus sampling to balance diversity and coherence. |
| Approach: | They propose a training-free decoding strategy that uses a model with a low perplexity score to select the trial with the lowest perplexities as the most probable and reliable path. |
| Outcome: | The proposed approach outperforms existing standard decoding strategies consistently by a clear margin. |
From Remembering to Metacognition: Do Existing Benchmarks Accurately Evaluate LLMs? (2025.findings-emnlp)
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Geng Zhang, Yizhou Ying, Sihang Jiang, Jiaqing Liang, Guanglei Yue, Yifei Fu, Hailin Hu, Yanghua Xiao
| Challenge: | Existing benchmark datasets focus on low-level cognitive tasks while providing limited coverage of higher-level reasoning skills. |
| Approach: | They analyze the cognitive depth of popular LLM benchmarks using Bloom’s Taxonomy to evaluate both the cognitive and knowledge dimensions. |
| Outcome: | The results show that incorporating higher-level cognitive instructions into the current instruction fine-tuning process improves model performance. |
Distorted or Fabricated? A Survey on Hallucination in Video LLMs (2026.findings-acl)
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| Challenge: | Despite significant advances in video-language modeling, hallucinations remain a persistent challenge in video large language models. |
| Approach: | They present a systematic taxonomy that categorizes hallucinations into two core types: dynamic distortion and content fabrication. |
| Outcome: | The proposed taxonomy categorizes hallucinations into two core types: dynamic distortion and content fabrication. |
How Do Large Language Models Perform in Dynamical System Modeling (2025.findings-naacl)
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| Challenge: | Recent data-driven methods often use graph neural networks (GNNs) to learn interactions between objects. |
| Approach: | They propose prompting techniques for dynamical system modeling and evaluate their performance . they find that large language models demonstrate competitive performance without training . |
| Outcome: | The proposed methods show competitive performance without training compared to state-of-the-art methods in dynamical system modeling. |
Protein Large Language Models: A Comprehensive Survey (2025.findings-emnlp)
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Yijia Xiao, Wanjia Zhao, Junkai Zhang, Yiqiao Jin, Han Zhang, Zhicheng Ren, Renliang Sun, Haixin Wang, Guancheng Wan, Pan Lu, Xiao Luo, Yu Zhang, James Zou, Yizhou Sun, Wei Wang
| Challenge: | Existing studies focus on specific aspects or applications, but this study provides a comprehensive overview of Protein-specific large language models. |
| Approach: | This paper proposes a structured taxonomy of state-of-the-art ProteinLLMs . they analyze how they leverage large-scale protein sequence data for improved accuracy . |
| Outcome: | The proposed model covers their architectures, training datasets, evaluation metrics, and diverse applications. |
Automated Molecular Concept Generation and Labeling with Large Language Models (2025.coling-main)
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| Challenge: | Concept-based models lack explainability and need predefined concepts and manual labeling in molecular science. |
| Approach: | They propose a framework that leverages Large Language Models to generate and label predictive molecular concepts without human input. |
| Outcome: | The proposed framework outperforms existing models on several benchmarks while maintaining explainability and allowing easy intervention. |
Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs? (2025.findings-acl)
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Qingyuan Liang, Zhao Zhang, Zeyu Sun, Zheng Lin, Qi Luo, Xiao Yueyi, Yizhou Chen, Yuqun Zhang, Haotian Zhang, Lu Zhang, Chenbin Chenbin, Yingfei Xiong
| Challenge: | Existing research demonstrates the effectiveness of grammar-based code representations in small-scale models, showing their ability to reduce syntax errors and enhance performance. |
| Approach: | They develop a series of billion-scale grammar-based code representations that incorporate grammar rules into the code generation process. |
| Outcome: | Experiments on HumanEval and MBPP show that grammar-based representations reduce syntax errors and improve performance even in billion-scale models. |
FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion (2024.findings-acl)
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| Challenge: | Existing work models taxonomy concepts as vectors or geometric objects, but fuzzy sets are efficient for concept modeling. |
| Approach: | They propose a set representation learning task based on fuzzy set approximation . they demonstrate remarkable improvements in taxonomy expansion using FUSE . |
| Outcome: | The proposed framework improves taxonomy expansion performance by 23% over baselines. |
Exploring the Hidden Reasoning Process of Large Language Models by Misleading Them (2025.findings-emnlp)
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Guanyu Chen, Peiyang Wang, Yizhou Jiang, Yuqian Liu, Chujie Zhao, Ying Fang, Tianren Zhang, Feng Chen
| Challenge: | Existing large language models can perform abstract reasoning tasks but are they actually engaging in rule-based reasoning beyond mere memorization? |
| Approach: | They propose a method to examine whether large language models perform abstract reasoning . they fine-tune the model to learn those contradictory rules and assess its generalization ability . |
| Outcome: | The proposed approach examines whether large language models perform abstract reasoning by altering their original understanding of fundamental rules. |