Papers by Xu Liangyu
POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering (2026.acl-long)
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| Challenge: | Existing chart understanding benchmarks are overwhelmingly English-centric, limiting their accessibility and relevance to global audiences. |
| Approach: | They propose a multilingual chart question answering benchmark that enables efficient multilingual generation via data translation and code reuse. |
| Outcome: | The proposed benchmark systematically evaluates multilingual chart understanding on state-of-the-art LVLMs and shows a significant performance gap between English and other languages. |
FRAME: Boosting LLMs with A Four-Quadrant Multi-Stage Pretraining Strategy (2025.findings-acl)
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Xuemiao Zhang, Feiyu Duan, Xu Liangyu, Yongwei Zhou, Sirui Wang, Rongxiang Weng, Jingang Wang, Xunliang Cai
| Challenge: | Multi-stage pretraining methods lack quantitative criteria for data partitioning and instead rely on intuitive heuristics. |
| Approach: | They propose a Four-quadRAnt Multi-stage prEtraining strategy that partitions data into four quadrants to achieve significant loss reductions four times. |
| Outcome: | The proposed strategy achieves 16.8% improvement over random across MMLU and CMMLU for the 3B model. |
Robustness of Learning from Task Instructions (2023.findings-acl)
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| Challenge: | traditional supervised learning mostly works on individual tasks and requires training on a large set of task-specific examples. |
| Approach: | a new study investigates the system robustness when instructions are manipulated and paraphrased . task instructions give the model the definition of the task and allow it to output the appropriate answer . |
| Outcome: | a new study shows that supervised learning is robust when instructions are manipulated, paraphrased or iii from different levels of conciseness. |
RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis (2025.findings-acl)
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Pengzuo Wu, Yuhang Yang, Guangcheng Zhu, Chao Ye, Hong Gu, Xu Lu, Ruixuan Xiao, Bowen Bao, Yijing He, Liangyu Zha, Wentao Ye, Junbo Zhao, Haobo Wang
| Challenge: | Existing benchmarks for large language models focus on simple, flat table structures. |
| Approach: | They propose a benchmark to evaluate the performance of both Large Language Models and Multimodal LLMs across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
| Outcome: | The proposed benchmark evaluates the performance of LLMs and Multimodal LLM models across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
FIRE: Flexible Integration of Data Quality Ratings for Effective Pretraining (2025.emnlp-main)
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| Challenge: | Existing methods to evaluate data quality rely on heuristic techniques or single quality signals. |
| Approach: | They propose a framework for integrating multiple data quality raters that integrates multiple quality signals into a unified space and provides a comprehensive quality signal for each data point. |
| Outcome: | The proposed framework outperforms existing methods and boosts model performance across a wide range of downstream tasks while requiring less than 37.5% tokens to reach the target performance. |
MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments (2026.acl-long)
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Quyu Kong, Xu Zhang, Zhenyu Yang, Nolan Gao, Chen Liu, Panrong Tong, Chenglin Cai, Hanzhang Zhou, Jianan Zhang, Liangyu Chen, Zhidan Liu, Steven Hoi, Yue Wang
| Challenge: | AndroidWorld is the dominant mobile GUI agent evaluation benchmark, but its success rates are low . despite reproducible emulator environment, it lacks key application categories such as e-commerce and enterprise communication. |
| Approach: | They propose a benchmark for mobile GUI agents that reflects real-world usage through long-horizon, cross-application workflows. |
| Outcome: | The proposed framework achieves over 90% success rates, while AndroidWorld is the dominant benchmark. |
Preference Curriculum: LLMs Should Always Be Pretrained on Their Preferred Data (2025.findings-acl)
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Xuemiao Zhang, Xu Liangyu, Feiyu Duan, Yongwei Zhou, Sirui Wang, Rongxiang Weng, Jingang Wang, Xunliang Cai
| Challenge: | Existing methods of uniformly sampling data throughout the pretraining process are suboptimal because they overlook the model's evolving data preferences. |
| Approach: | They propose a Perplexity Difference (PD) based Preference Curriculum learning framework which perceives and uses the data preferred by LLMs as their capabilities improve . they propose PDPC to complete the arrangement of the dataset offline and ensure continuous training without interruption. |
| Outcome: | The proposed framework surpasses baselines on 1.3B and 3B models and achieves an increased average accuracy of over 8.1% across MMLU and CMMLU. |
A Survey of Large Models in Sports (2026.findings-acl)
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| Challenge: | Increasing interest in sports has led to the rapid advancement of large models, particularly multimodal large language models (MLLMs) . linguistic intelligence is a key component of large-model-driven sports intelligence . |
| Approach: | They propose to establish a foundation for advancing research and practical development of large-model-driven sports intelligence. |
| Outcome: | The proposed model-driven sports intelligence will be able to process and generate sports-related language effectively and process multiple data modalities. |