Papers by Yuan Ling
Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset (2026.findings-acl)
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Baode Wang, Biao Wu, Weizhen Li, Meng Fang, Zuming Huang, Jun Huang, Yanjie Liang, Haozhe Wang, Ling Chen, Wei Chu, Yuan Qi
| Challenge: | Existing supervised fine-tuning methods struggle to generalize across document types, leading to poor performance. |
| Approach: | They propose layoutRL, a reinforcement learning framework that optimizes layout understanding through composite rewards integrating normalized edit distance, paragraph count accuracy, and reading order preservation. |
| Outcome: | The proposed model outperforms specialized document parsing systems and general-purpose vision-language models on a broad range of document types, languages, and structural complexities. |
Efficient Pretraining Data Selection for Language Models via Multi-Actor Collaboration (2025.acl-long)
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Tianyi Bai, Ling Yang, Zhen Hao Wong, Fupeng Sun, Xinlin Zhuang, Jiahui Peng, Chi Zhang, Lijun Wu, Qiu Jiantao, Wentao Zhang, Binhang Yuan, Conghui He
| Challenge: | Efficient data selection is crucial to accelerate the pretraining of language models . limited research has addressed the inherent conflicts between data selection methods . |
| Approach: | They propose a multi-actor collaborative data selection mechanism that prioritizes data based on its specific criterion and updates prioritization rules using the current state of the model. |
| Outcome: | The proposed model accelerates convergence in LM pretraining and achieves an average relative performance gain of 10.5% across multiple language model benchmarks. |
ShopperBench: A Benchmark for Personalized Shopping with Persona-Guided Simulation (2026.eacl-industry)
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| Challenge: | Existing evaluation frameworks lack mechanisms to assess Personalized shopping agents' ability to adapt their strategies to heterogeneous user preferences and decisionmaking patterns. |
| Approach: | They propose a persona-guided benchmark that augments shopping trajectories with personas . they propose persona Fidelity, Persona-Query Alignment, and Path Consistency . |
| Outcome: | The proposed benchmark captures how shopper types navigate product search and selection . it measures persona Fidelity, Persona-Query Alignment, and Path Consistency . |
CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation (2026.acl-long)
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| Challenge: | Existing evaluation metrics for radiology report generation focus on lexical overlap and entity matching. |
| Approach: | They propose a benchmark to evaluate the fine-grained factual consistency of CT reports . they use a question-answering process to query a machine-generated report . |
| Outcome: | The proposed benchmark evaluates the fine-grained factual consistency of CT reports . it correlates better with expert clinical assessment and is more sensitive to errors . |
Identifying the Achilles’ Heel: An Iterative Method for Uncovering Factual Errors in Large Language Models (2026.findings-acl)
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Wenxuan Wang, Yuk-Kit Chan, Zixuan Ling, Shi Juluan, Youliang Yuan, Jen-tse Huang, Yifei Zhang, Wenxiang Jiao, Zhaopeng Tu, Michael R. Lyu
| Challenge: | Current methods for evaluating LLMs’ veracity are limited by the need for extensive human labor, test data contamination, or limited scope, hindering efficient and effective exposure of errors. |
| Approach: | They propose a framework that extracts fact triplets to generate diverse question types using rule-based natural language processing techniques. |
| Outcome: | The proposed framework can trigger factual errors in up to 55% of questions in large LLMs while maintaining coverage of questions. |
Learning to Ask: When LLM Agents Meet Unclear Instruction (2025.emnlp-main)
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Wenxuan Wang, Shi Juluan, Zixuan Ling, Yuk-Kit Chan, Chaozheng Wang, Cheryl Lee, Youliang Yuan, Jen-tse Huang, Wenxiang Jiao, Michael R. Lyu
| Challenge: | Despite their impressive capabilities, LLMs struggle with complex computations and delivering accurate, timely information. |
| Approach: | They propose a framework that prompts LLM agents to ask questions when they encounter obstacles due to unclear instructions and an automated evaluation tool called ToolEvaluator. |
| Outcome: | The proposed framework outperforms existing frameworks for tool learning in the Noisy ToolBench. |
Unified Dual-view Cognitive Model for Interpretable Claim Verification (2021.acl-long)
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| Challenge: | Existing studies constructing direct interactions between the claim and each single user response to capture evidence have shown remarkable success in interpretable claim verification. |
| Approach: | They propose a Dual-view model based on the views of Collective and Individual Cognition (CICD) that captures word-level semantics based . on individual cognition, they adjust the proportion between them to generate global evidence. |
| Outcome: | The proposed model is based on the views of collective and individual cognition and achieves state-of-the-art performance on three benchmark datasets. |
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference (N18-1)
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Reza Ghaeini, Sadid A. Hasan, Vivek Datla, Joey Liu, Kathy Lee, Ashequl Qadir, Yuan Ling, Aaditya Prakash, Xiaoli Fern, Oladimeji Farri
| Challenge: | Existing approaches to natural language inference rely on simple reading mechanisms for independent encoding of the premise and hypothesis. |
| Approach: | They propose a novel bidirectional dependent reading network to efficiently model the relationship between a premise and a hypothesis during encoding and inference. |
| Outcome: | The proposed model outperforms existing methods by a considerable margin on the Stanford Natural Language Inference (SNLI) dataset. |
Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection (D19-1)
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| Challenge: | Existing methods for detecting fake news use shared features as complementarity features without selection. |
| Approach: | They propose a sifted multi-task learning method with a selected sharing layer for fake news detection. |
| Outcome: | The proposed method boosts the F1-score by more than 0.87%, 1.31% on two public and widely used competition datasets. |