Papers by Ruixuan Xiao
On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation. |
| Approach: | They propose a generic workflow for LLM-driven synthetic data generation. |
| Outcome: | The proposed workflows highlight gaps in existing research and outline avenues for future studies. |
D.Va: Validate Your Demonstration First Before You Use It (2025.acl-long)
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| Challenge: | In-context learning (ICL) heavily relies on selecting effective demonstrations to achieve outputs that better align with the expected results. |
| Approach: | They propose a method which integrates a demonstration validation perspective into this field and integrates it into the learning paradigm. |
| Outcome: | The proposed method surpasses all retrieval-based in-context learning techniques across both natural language understanding (NLU) and natural language generation (NLG) tasks. |
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. |
FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models (2023.emnlp-main)
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| Challenge: | Modern machine learning models require a huge collection of precisely labeled data, which can be labor-intensive and time-consuming. |
| Approach: | They propose a collaborative learning framework that interactively distills and filters the task-specific knowledge from LLMs. |
| Outcome: | The proposed framework improves zero-shot performance on eight benchmark datasets without human supervision. |
FlowBench: Revisiting and Benchmarking Workflow-Guided Planning for LLM-based Agents (2024.findings-emnlp)
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| Challenge: | LLM-based agents are susceptible to undesired planning hallucinations when lacking specific knowledge for expertise-intensive tasks. |
| Approach: | They propose a benchmark to evaluate the efficacy of workflow-guided agent planning by formalizing different formats of workflow knowledge. |
| Outcome: | The proposed benchmark aims to improve the planning reliability of LLM-based agents by incorporating external workflow-related knowledge. |
OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)
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Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu
| Challenge: | a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide. |
| Approach: | They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions . |
| Outcome: | The proposed agents are based on operating systems (OS) and operating systems frameworks. |
Large Margin Representation Learning for Robust Cross-lingual Named Entity Recognition (2025.acl-long)
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| Challenge: | Existing approaches to name entity recognition neglect distribution skewness and pseudo-label bias . despite promising results, current approaches neglect these problems . |
| Approach: | They propose a framework that optimizes an adaptively reweighted contrastive loss to handle class skewness and pseudo-label bias. |
| Outcome: | The proposed framework outperforms existing methods on multiple benchmarks. |