Papers by Yasha Wang
TC–RAG: Turing–Complete RAG’s Case study on Medical LLM Systems (2025.acl-long)
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Xinke Jiang, Yue Fang, Rihong Qiu, Haoyu Zhang, Yongxin Xu, Hao Chen, Wentao Zhang, Ruizhe Zhang, Yuchen Fang, Xinyu Ma, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Existing approaches to RAG neglect system state variables, resulting in poor performance and erroneous knowledge accumulation. |
| Approach: | They propose a framework that incorporates a Turing Complete System to manage state variables and manage retrieval halting. |
| Outcome: | The proposed framework improves on seven real-world healthcare datasets and shows that it is more accurate than existing methods. |
FOREVER: Forgetting Curve-Inspired Memory Replay for Language Model Continual Learning (2026.acl-long)
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Yujie Feng, Hao Wang, Jian Li, Xu Chu, Zhaolu Kang, Yiran Liu, Yasha Wang, Philip S. Yu, Xiao-Ming Wu
| Challenge: | Continual learning (CL) for large language models (LLMs) aims to enable sequential knowledge acquisition without catastrophic forgetting. |
| Approach: | They propose a framework that aligns replay schedules with a model-centric notion of time. |
| Outcome: | Experiments on three benchmarks show that FOREVER consistently mitigates catastrophic forgetting. |
3DS: Medical Domain Adaptation of LLMs via Decomposed Difficulty-based Data Selection (2025.emnlp-main)
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Hongxin Ding, Yue Fang, Runchuan Zhu, Xinke Jiang, Jinyang Zhang, Yongxin Xu, Weibin Liao, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Effective domain adaptation typically involves supervised fine-tuning on carefully selected instruction-tuned data. |
| Approach: | They propose a model-centric data selection framework that aligns data selection with the model’s knowledge distribution to improve model performance. |
| Outcome: | The proposed framework outperforms existing methods by up to 2.97% accuracy in the healthcare domain. |
HyKGE: A Hypothesis Knowledge Graph Enhanced RAG Framework for Accurate and Reliable Medical LLMs Responses (2025.acl-long)
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Xinke Jiang, Ruizhe Zhang, Yongxin Xu, Rihong Qiu, Yue Fang, Zhiyuan Wang, Jinyi Tang, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Recent approaches suffer from insufficient and repetitive knowledge retrieval, tedious and time-consuming query parsing, and monotonous knowledge utilization. |
| Approach: | They propose a retrieval-augmented generation framework which leverages LLMs’ powerful reasoning capacity to compensate for the incompleteness of user queries. |
| Outcome: | The proposed framework improves the accuracy and reliability of Large Language Models (LLMs) by combining the rich knowledge of LLMs with Hypothesis Outputs. |
ITAKE: Interactive Unstructured Text Annotation and Knowledge Extraction System with LLMs and ModelOps (2024.acl-demos)
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| Challenge: | Unstructured text data contains a large amount of valuable knowledge, but there are many tools that do not meet the needs of actual business. |
| Approach: | They propose an unstructured text annotation and knowledge extraction system that integrates Large Language Models and ModelOps to improve model supervision and performance. |
| Outcome: | The proposed system integrates large language models and ModelOps to improve performance in low-resource contexts. |
Recurrent Knowledge Identification and Fusion for Language Model Continual Learning (2025.acl-long)
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Yujie Feng, Xujia Wang, Zexin Lu, Shenghong Fu, Guangyuan Shi, Yongxin Xu, Yasha Wang, Philip S. Yu, Xu Chu, Xiao-Ming Wu
| Challenge: | Continual learning (CL) is crucial for large language models without costly retraining. |
| Approach: | They propose a framework for recurrent knowledge identification and fusion that enables dynamic estimation of parameter importance distributions to enhance knowledge transfer. |
| Outcome: | The proposed framework mitigates catastrophic forgetting and enhances knowledge transfer. |
Enhancing Neural Topic Model with Multi-Level Supervisions from Seed Words (2023.findings-acl)
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| Challenge: | Existing topic seed words are difficult to incorporate into topic models due to the semantic diversity of natural language. |
| Approach: | They propose a neural topic model enhanced with supervisions from seed words on word and document levels. |
| Outcome: | The proposed model outperforms the state-of-the-art seeded topic models in terms of topic quality and classification accuracy. |
Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning (2025.acl-long)
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Yongxin Xu, Ruizhe Zhang, Xinke Jiang, Yujie Feng, Yuzhen Xiao, Xinyu Ma, Runchuan Zhu, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Existing methods for integrating internal and external knowledge lack effective control mechanisms for generating hallucinations and dealing with outdated knowledge. |
| Approach: | They propose a framework that decouples, identifies, and purposefully optimizes parameter subspaces related to adherence and robustness. |
| Outcome: | The proposed framework decouples, identifies, and purposefully optimizes parameter subspaces related to adherence and robustness. |
Combating Label Sparsity in Short Text Topic Modeling via Nearest Neighbor Augmentation (2024.findings-acl)
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| Challenge: | Existing topic models suffer from poor performance when applied to short text contents due to the limited length of a single topic. |
| Approach: | They propose a neural short text topic model that augments reconstruction labels with k-nearest documents to complement relevant but unobserved words. |
| Outcome: | The proposed model outperforms the state-of-the-art models on multiple public short-text datasets and can derive high-quality topics and document representations. |
DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Search (2026.acl-long)
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Zhibang Yang, Xinke Jiang, Rihong Qiu, Ruiqing Li, Yihang Zhang, Yue Fang, Yongxin Xu, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Existing methods for ambiguous queries struggle to retrieve high-quality documents . DFAMS outperforms advanced FR methods by 14.37% in knowledge classification accuracy . |
| Approach: | They propose a framework that leverages dynamic information flow to identify latent query intents and construct semantically aligned knowledge partitions for accurate retrieval across heterogeneous sources. |
| Outcome: | The proposed framework outperforms existing methods in classification accuracy and retrieval recall tests. |
GeoEdit: Geometric Knowledge Editing for Large Language Models (2025.emnlp-main)
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Yujie Feng, Li-Ming Zhan, Zexin Lu, Yongxin Xu, Xu Chu, Yasha Wang, Jiannong Cao, Philip S. Yu, Xiao-Ming Wu
| Challenge: | Existing training-based model editing methods struggle to incorporate new knowledge while preserving unrelated general knowledge. |
| Approach: | They propose a framework that uses geometric relationships to differentiate between neurons associated with new knowledge updates and those related to general knowledge perturbations. |
| Outcome: | The proposed framework avoids updating neurons with directions approximately orthogonal to existing knowledge, thus preserving the model’s generalization ability. |
AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning (2025.emnlp-main)
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Yujie Feng, Jian Li, Xiaoyu Dong, Pengfei Xu, Xiaohui Zhou, Yujia Zhang, Zexin Lu, Yasha Wang, Alan Zhao, Xu Chu, Xiao-Ming Wu
| Challenge: | Recent model merging-based methods struggle to effectively manage the trade-off between learning new knowledge and preventing catastrophic forgetting. |
| Approach: | They propose a model merging framework that utilizes learning and forgetting signals from the training trajectory to dynamically monitor the model’s training status. |
| Outcome: | The proposed framework achieves significant performance improvements over existing state-of-the-art methods on three CL benchmarks with various model sizes (from 770M to 13B). |
ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs (2026.acl-long)
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Hongxin Ding, Baixiang Huang, Yue Fang, Weibin Liao, Xinke Jiang, Jinyang Zhang, Yinghao Zhu, Zheng Li, Liantao Ma, Junfeng Zhao, Yasha Wang
| Challenge: | Existing medical Large Language Models (LLMs) follow a reactive paradigm, risking diagnostic errors by answering before seeking sufficient details. |
| Approach: | They propose a reinforcement learning framework that transitions LLMs toward a proactive paradigm, enabling them to ask clinically valuable questions before decision-making. |
| Outcome: | Experiments on partial-information medical benchmarks show that ProMed outperforms state-of-the-art methods by 6.29% on average and delivers a 54.45% gain over the reactive paradigm. |