Papers by Ang Wang
Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models (2024.acl-long)
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Changyu Chen, Xiting Wang, Ting-En Lin, Ang Lv, Yuchuan Wu, Xin Gao, Ji-Rong Wen, Rui Yan, Yongbin Li
| Challenge: | Despite the advances in large language models, they still face difficulties with multi-step reasoning tasks. |
| Approach: | They propose a method that randomly masks certain tokens within the chain of thought to improve model accuracy by 5% over standard supervised fine-tuning. |
| Outcome: | The proposed method improves accuracy and accuracy by 5% over standard fine-tuning with a few codes modified. |
Union-of-Experts: Neurons in Mixture-of-Experts are Secretly Routers (2026.acl-long)
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| Challenge: | Mixture-of-Experts (MoE) models rely on an external router to assign tokens to experts, resulting in suboptimal performance. |
| Approach: | They propose an MoE variant that performs "expert-autonomous routing" by pre-designating a fraction of neurons within each expert as "routing neurons" they pre-train UoE models with up to 3B parameters and show they outperform traditional MoEs with matched efficiency. |
| Outcome: | The proposed model outperforms existing models with 3B parameters and provides valuable insights into expert-autonomous selection and the broader routing mechanisms of MoE models. |
Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training (2022.emnlp-industry)
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Taolin Zhang, Junwei Dong, Jianing Wang, Chengyu Wang, Ang Wang, Yinghui Liu, Jun Huang, Yong Li, Xiaofeng He
| Challenge: | Existing knowledge-enhanced pre-trained language models (KEPLMs) can capture internal knowledge, but can't understand external background knowledge. |
| Approach: | They propose to use Chinese knowledge-enhanced pre-trained language models to improve context-aware representations via learning from structured relations in knowledge bases. |
| Outcome: | Experiments show that Chinese knowledge-enhanced pre-trained language models outperform strong baselines over various benchmark NLP tasks and in different model sizes. |
Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents (2024.acl-long)
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Shihan Deng, Weikai Xu, Hongda Sun, Wei Liu, Tao Tan, Liujianfeng Liujianfeng, Ang Li, Jian Luan, Bin Wang, Rui Yan, Shuo Shang
| Challenge: | Existing benchmarks for LLM-based mobile agents are insufficient to evaluate their capabilities. |
| Approach: | They propose a benchmark to evaluate LLM-based mobile agents' planning capabilities . they expand UI operations by incorporating 103 APIs to accelerate task completion . |
| Outcome: | The proposed benchmarks are based on 103 collected APIs and real user queries . the data is categorized into three distinct groups: SAST, SAMT, and MAMT . |
UniLR: Unleashing the Power of LLMs on Multiple Legal Tasks with a Unified Legal Retriever (2025.acl-long)
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Ang Li, Yiquan Wu, Yifei Liu, Ming Cai, Lizhi Qing, Shihang Wang, Yangyang Kang, Chengyuan Liu, Fei Wu, Kun Kuang
| Challenge: | Existing retrieval methods are designed for general domains, struggling with legal knowledge, or tailored for specific legal tasks, unable to handle diverse legal knowledge types. |
| Approach: | They propose a novel retrieval method that integrates specialized knowledge into LLMs. |
| Outcome: | The proposed method can perform multiple legal retrieval tasks for LLMs. |
StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason (2026.acl-long)
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| Challenge: | Reinforcement learning with verifiable rewards (RLVR) approaches face two challenges: the near-miss reward problem and exploration stagnation. |
| Approach: | They propose an algorithm that partitions valid reasoning chains into reasoning steps using multi-level stepwise hints. |
| Outcome: | The proposed method outperforms competing RLVR enhancement methods across six mathematical benchmarks and two out-of-domain benchmarks. |
CoEvo: Coevolution of LLM and Retrieval Model for Domain-Specific Information Retrieval (2025.emnlp-main)
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Ang Li, Yiquan Wu, Yinghao Hu, Lizhi Qing, Shihang Wang, Chengyuan Liu, Tao Wu, Adam Jatowt, Ming Cai, Fei Wu, Kun Kuang
| Challenge: | Recent methods to enhance queries by generating intermediary elements can degrade retrieval performance . combining LLMs and retrievers can be difficult, resulting in unreliable or irrelevant intermediaries . |
| Approach: | They propose a framework that facilitates the coevolution of large language models and retrieval models. |
| Outcome: | The proposed framework facilitates the coevolution of LLMs and retrieval models. |
MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding (2024.findings-emnlp)
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Qinzhuo Wu, Weikai Xu, Wei Liu, Tao Tan, Liujian Liujianfeng, Ang Li, Jian Luan, Bin Wang, Shuo Shang
| Challenge: | Recent mobile AI agents based on VLMs lack basic mobile capabilities due to their pre-trained nature. |
| Approach: | They propose a mobile AI agent based on VLMs that includes additional pre-training stages to enhance both intra- and inter-UI understanding. |
| Outcome: | The proposed model outperforms existing VLMs on the Chinese mobile dataset Mobile3M . |
Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition (2026.findings-acl)
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| Challenge: | Cloud-hosted Large Language Models (LLMs) offer unmatched reasoning capabilities and dynamic knowledge, yet submitting raw queries to these services can expose sensitive user intent. |
| Approach: | They propose a framework that formulates the trade-off between knowledge utility and privacy as a strategic game. |
| Outcome: | The proposed framework reduces intent leakage while maintaining high-fidelity answer quality. |
HoPE: A Novel Positional Encoding Without Long-Term Decay for Enhanced Context Awareness and Extrapolation (2025.acl-long)
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| Challenge: | Existing positional encodings exhibit long-term decay, based on an entrenched and long-standing opinion that tokens farther away from the current position carry less relevant information. |
| Approach: | They propose a high-frequency rotary position encoding (HoPE) that replaces specific components in RoPE with position-independent ones, retaining only high- frequency signals. |
| Outcome: | The proposed method exhibits greater robustness to the out-of-distribution behavior in attention patterns during extrapolation. |
Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents (2025.acl-long)
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| Challenge: | Large language models (LLMs) are becoming increasingly popular in education, enabling researchers to simulate students' learning patterns and learning patterns. |
| Approach: | They propose a training-free framework for student simulation that takes into account student cognitive diversity and realism. |
| Outcome: | The proposed model outperforms baseline models and achieves 100% improvement in simulation accuracy and realism. |
Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibration (2025.naacl-long)
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Ang Li, Jingqian Zhao, Bin Liang, Lin Gui, Hui Wang, Xi Zeng, Xingwei Liang, Kam-Fai Wong, Ruifeng Xu
| Challenge: | Large language models generate biased stances due to spurious correlations and preference towards certain individuals and topics. |
| Approach: | They propose a counterfactual Augmented Calibration Network to calibrate potential bias in stance detection of large language models. |
| Outcome: | The proposed calibration network can mitigate biases of large language models, achieving state-of-the-art results. |