Papers by Li Yangning
TestNUC: Enhancing Test-Time Computing Approaches and Scaling through Neighboring Unlabeled Data Consistency (2025.acl-long)
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Henry Peng Zou, Zhengyao Gu, Yue Zhou, Yankai Chen, Weizhi Zhang, Liancheng Fang, Yibo Wang, Yangning Li, Kay Liu, Philip S. Yu
| Challenge: | Test-time computing approaches that leverage additional computational resources during inference have been proven effective in enhancing large language model performance. |
| Approach: | They propose a linearly scaling approach that leverages local consistency of neighboring unlabeled data to improve test-time predictions. |
| Outcome: | The proposed approach outperforms baseline methods such as prompting and self-consistency across eight datasets and performs robustly across embedding models. |
Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters (2024.acl-long)
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Yinghui Li, Zishan Xu, Shaoshen Chen, Haojing Huang, Yangning Li, Shirong Ma, Yong Jiang, Zhongli Li, Qingyu Zhou, Hai-Tao Zheng, Ying Shen
| Challenge: | Existing studies focus on misspelled characters, ignoring faked characters which are more common and difficult to correct. |
| Approach: | They propose to use Chinese character checking to identify and correct wrong characters in texts by human annotation. |
| Outcome: | The proposed dataset is the first real-world visual and the largest human-crafted dataset for the Chinese character checking scenario. |
CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction (2023.emnlp-main)
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| Challenge: | Evaluating the performance of Grammatical Error Correction systems is a challenging task due to its subjectivity. |
| Approach: | They propose a method to evaluate GEC systems in multi-reference evaluation setting . they use consistent edit boundaries to eliminate bias caused by inconsistent edit boundaries . |
| Outcome: | The proposed evaluation metric eliminates bias caused by inconsistent edit boundaries on six English reference sets. |
Depth Aware Hierarchical Replay Continual Learning for Knowledge Based Question Answering (2024.lrec-main)
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| Challenge: | Continual learning models adapt well to the latest data but lose ability to remember past data due to changes in the data source. |
| Approach: | They propose a hierarchical replay framework that allows models to keep a small memory of previous learned data that uses replay. |
| Outcome: | The proposed model outperforms previous continual learning methods in mitigating catastrophic forgetting. |
LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)
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Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu
| Challenge: | Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. |
| Approach: | They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety. |
| Outcome: | The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system. |
A Survey of RAG-Reasoning Systems in Large Language Models (2025.findings-emnlp)
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Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, Philip S. Yu
| Challenge: | a survey of RAG-based reasoning-based approaches shows that it is not effective for multi-step inferences. |
| Approach: | They map how advanced reasoning optimizes each stage of RAG . they show how retrieved knowledge supply missing premises and expand context for complex inference . |
| Outcome: | The proposed frameworks achieve state-of-the-art across knowledge-intensive benchmarks. |
A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check (2023.findings-emnlp)
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| Challenge: | Recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks. |
| Approach: | They propose to decompose Chinese Spelling Check into detection, reasoning, and searching subtasks and to train a module that is compatible with existing CSC models. |
| Outcome: | The proposed module can be trained for one model and benefit other models. |
Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning (2025.findings-emnlp)
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Yangning Li, Tingwei Lu, Yinghui Li, Yankai Chen, Wei-Chieh Huang, Wenhao Jiang, Hui Wang, Hai-Tao Zheng, Philip S. Yu
| Challenge: | Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) current methods suffer from the curriculum rigidity, resulting in a fixed and potentially sub-optimal learning trajectory. |
| Approach: | a framework for efficient instruction tuning is proposed to address the issue of curriculum rigidity . current methods rely on static heuristic difficulty metrics and fail to adapt to evolving capabilities . |
| Outcome: | Efficient instruction tuning aims to enhance the ultimate performance of large language models . current methods suffer from the curriculum rigidity, resulting in a fixed learning trajectory . |
MKT: A Multi-Stage Knowledge Transfer Framework to Mitigate Catastrophic Forgetting in Multi-Domain Chinese Spelling Correction (2025.emnlp-industry)
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Peng Xing, Yinghui Li, Shirong Ma, Xinnian Liang, Haojing Huang, Yangning Li, Shu-Yu Guo, Hai-Tao Zheng, Wenhao Jiang, Ying Shen
| Challenge: | Chinese Spelling Correction (CSC) is a model that detects and corrects spelling errors in given sentences. |
| Approach: | They propose a model-agnostic model with an evolving teacher model and dynamic distillation weights for knowledge transfer in each domain rather than focusing solely on new domain knowledge. |
| Outcome: | The proposed model-agnostic framework is based on an evolving teacher model and dynamic distillation weights for knowledge transfer in each domain, rather than focusing solely on new domain knowledge. |
Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction (2025.acl-industry)
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Yinghui Li, Shang Qin, Jingheng Ye, Haojing Huang, Yangning Li, Shu-Yu Guo, Libo Qin, Xuming Hu, Wenhao Jiang, Hai-Tao Zheng, Philip S. Yu
| Challenge: | Recent studies have shown that Large Language Models’ performance as correctors on Chinese Grammatical Error Correction (CGEC) remains unsatisfactory due to the challenging nature of the task. |
| Approach: | They propose a training framework EXAM that uses LLMs as explainers to enhance CGEC small models and a novel evaluation method SEE that utilizes LLM as evaluators to bring more reasonable evaluations. |
| Outcome: | The proposed methods improve the performance of LLMs on Chinese Grammatical Error Correction (CGEC) task. |
ProductAgent: Benchmarking Conversational Product Search Agent with Asking Clarification Questions (2025.emnlp-industry)
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| Challenge: | Recent advances in conversational information seeking (CIS) suggest a remedy for the lack of interactive clarification when people face unfamiliar domains. |
| Approach: | They propose a fully autonomous conversational information-seeking agent that couples large language models with a set of domain-specific tools to provide product demand clarification. |
| Outcome: | The proposed agent can iterate over 2,000 automatically generated sessions and score high on real-world evaluations without human annotation. |
Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction (2022.findings-emnlp)
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Shirong Ma, Yinghui Li, Rongyi Sun, Qingyu Zhou, Shulin Huang, Ding Zhang, Li Yangning, Ruiyang Liu, Zhongli Li, Yunbo Cao, Haitao Zheng, Ying Shen
| Challenge: | Chinese Grammatical Error Correction (CGEC) is a challenging NLP task and a common application in human daily life. |
| Approach: | They propose a linguistic rules-based approach to construct large-scale CGEC training corpora with automatically generated grammatical errors. |
| Outcome: | The proposed method improves performance of existing CGEC models and the benchmark is excellent resource for further development. |
MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correction (2023.findings-emnlp)
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| Challenge: | Various data augmentation strategies have been proposed to improve GEC models . high-quality parallel data for GEC is not as widely available . |
| Approach: | They propose a data augmentation approach that strategically augments real data by generating pseudo data. |
| Outcome: | The proposed approach significantly improves GEC models on English and Chinese datasets. |
Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking (2022.findings-emnlp)
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Yinghui Li, Shirong Ma, Qingyu Zhou, Zhongli Li, Li Yangning, Shulin Huang, Ruiyang Liu, Chao Li, Yunbo Cao, Haitao Zheng
| Challenge: | Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors. |
| Approach: | They propose a framework which renders Chinese Spell Checking model to learn heterogeneous knowledge from the dictionary in terms of phonetics, vision, and meaning. |
| Outcome: | The proposed framework renders the CSC model to learn heterogeneous knowledge from the dictionary in terms of phonetics, vision, and meaning. |
DAST: Context-Aware Compression in LLMs via Dynamic Allocation of Soft Tokens (2025.findings-acl)
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Shaoshen Chen, Yangning Li, Zishan Xu, Yongqin Zeng, Shunlong Wu, Xinshuo Hu, Zifei Shan, Xin Su, Jiwei Tang, Yinghui Li, Hai-Tao Zheng
| Challenge: | Existing semantic vector-based compression methods do not account for the intrinsic information density variations between context chunks, instead allocating soft tokens uniformly across context chunk. |
| Approach: | They propose a method that leverages the LLM's intrinsic understanding of contextual relevance to guide compression. |
| Outcome: | The proposed method surpasses state-of-the-art methods on long context tasks. |
Evaluating Robustness of Generative Search Engine on Adversarial Factoid Questions (2024.findings-acl)
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Xuming Hu, Xiaochuan Li, Junzhe Chen, Yinghui Li, Yangning Li, Xiaoguang Li, Yasheng Wang, Qun Liu, Lijie Wen, Philip Yu, Zhijiang Guo
| Challenge: | Existing large language models (LLMs)-backed generative search engines may not always be accurate. |
| Approach: | They propose to evaluate the robustness of retrieval-augmented generation in a realistic and high-risk setting where adversaries have only black-box system access. |
| Outcome: | The proposed model exhibits higher susceptibility to factual errors compared to LLMs without retrieval. |
Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety (2026.acl-long)
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Wei-Chieh Huang, Henry Peng Zou, Yaozu Wu, Dongyuan Li, Yankai Chen, Weizhi Zhang, Yangning Li, Angelo Zangari, Jizhou Guo, Chunyu Miao, Liancheng Fang, Langzhou He, Yinghui Li, Renhe Jiang, Philip S. Yu
| Challenge: | Existing deep research frameworks lack adequate evaluation procedures and stage-specific protections. |
| Approach: | They propose a framework with open-domain evaluation and a stage-wise safety benchmark to address this oversight. |
| Outcome: | The proposed framework improves defense success rates by 16.53% while reducing over-refusal rates to approximately 6%. |
Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances, Resources, and Future Directions (2025.findings-emnlp)
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Yaozu Wu, Dongyuan Li, Yankai Chen, Renhe Jiang, Henry Peng Zou, Wei-Chieh Huang, Yangning Li, Liancheng Fang, Zhen Wang, Philip S. Yu
| Challenge: | Large Language Models (LLMs) are used to assist with driving decisions, but they face limitations in perception and computational demands. |
| Approach: | They propose a survey of LLM-based multi-agent ADSs and their applications . they analyze agent-human interactions in scenarios where LLM agents engage with humans . |
| Outcome: | The proposed approach reduces human intervention and improves safety and efficiency. |
RAISE: Reinforced Adaptive Instruction Selection For Large Language Models (2025.findings-emnlp)
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Qingsong Lv, Yangning Li, Zihua Lan, Zishan Xu, Jiwei Tang, Tingwei Lu, Yinghui Li, Wenhao Jiang, Hong-Gee Kim, Hai-Tao Zheng, Philip S. Yu
| Challenge: | Existing selection methods rely on static, heuristic quality scores and are executed only once before training. |
| Approach: | They propose a dynamic selection framework that integrates selection into every training step. |
| Outcome: | The proposed framework integrates selection into every training step. |
The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking (2022.findings-acl)
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Yinghui Li, Qingyu Zhou, Yangning Li, Zhongli Li, Ruiyang Liu, Rongyi Sun, Zizhen Wang, Chao Li, Yunbo Cao, Hai-Tao Zheng
| Challenge: | Chinese Spell Checking (CSC) aims to detect and correct spelling errors, which are caused by the phonological or visual similarity. |
| Approach: | They propose an Error-driven COntrastive Probability Optimization framework to refine the knowledge representations of pre-trained language models to avoid predicting common characters. |
| Outcome: | Extensive experiments and detailed analyses on SIGHAN datasets demonstrate that ECOPO is simple yet effective. |