Papers by Weijie Liang
ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue (2026.findings-acl)
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Jiawei Shen, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Qingyu Niu, Guoqing Ma, Jingjiang Liu, Yidan Liang, Yilin Wang, Shimin Di, Jiajie Xu
| Challenge: | Existing approaches to multi-turn dialogues lack contextual consistency and dependencies, and models struggle to maintain factual faithfulness as interaction turns increase. |
| Approach: | They propose an adaptive context refactoring framework that monitors and reshapes the interaction history to mitigate contextual inertia and state drift. |
| Outcome: | The proposed model outperforms baselines while reducing token consumption. |
MedQPA-Gen: Medical Question Proposing and Answering for Report Generation (2026.findings-acl)
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Weijie Liang, Xiyue Zhu, Ruike Zhu, Chenhao Li, Cheng Tang, Zhiyu Liu, Zhihua Gong, Shirui Luo, Yudu Li, Volodymyr Kindratenko
| Challenge: | Existing training paradigms fail to explicitly target factual accuracy, resulting in inaccuracies and serious patient safety risks. |
| Approach: | They propose an automatic and generalizable report evaluation technique that uses question proposing and answering to enable controllable, structured reasoning grounded in medical domain knowledge and the factual correctness of the report. |
| Outcome: | The proposed method can improve human preference scores and perform better on downstream tasks. |
Wasserstein Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains (2020.emnlp-main)
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| Challenge: | Asymmetrical text matching is a fundamental problem in information retrieval and natural language processing. |
| Approach: | They propose a method that regularizes features vectors projected from different domains . WD-Match can be used to improve different text matching methods . |
| Outcome: | The proposed method outperforms existing methods and benchmarks on four datasets. |
VizoMem: A Visual-Textual Memory Framework for Efficient Long-Horizon Reasoning (2026.findings-acl)
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| Challenge: | Existing systems that use long-context modeling incur computational and memory overhead. |
| Approach: | They propose a visual memory framework that pre-rendered text into structured images and stored as visual notes for agentic systems. |
| Outcome: | The proposed system reduces token consumption while preserving effective long-term memory recall. |
Giving Control Back to Models: Enabling Offensive Language Detection Models to Autonomously Identify and Mitigate Biases (2024.findings-emnlp)
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| Challenge: | Existing models often rely on specific words to predict offensive content, compromising model fairness and potentially exacerbates biases against vulnerable and minority groups. |
| Approach: | They propose a bias self-awareness and data self-iteration framework to help models identify and mitigate biases by integrating multiple natural language processing techniques. |
| Outcome: | The proposed framework reduces false positive rate of models in in-distribution and out-of-difference tests, enhances model accuracy and fairness, and shows promising performance improvements on larger datasets. |
RSDA: Restoring Stale Data Affinity via Dynamic Renovation Strategy for Mitigating Data Scarcity (2026.acl-long)
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Yidan Liang, Jia Zhu, Weijie Shi, Hanghui Guo, Yue Cui, Jiawei Shen, Guoqing Ma, Jingjiang Liu, Qingyu Niu, Yilin Wang, Shimin Di, Jiajie Xu
| Challenge: | High-quality data is the cornerstone of advancing large language models, but the supply of premium data is nearing depletion, while vast stale corpora remain underutilized. |
| Approach: | They propose a framework to restore stale data affinity by quantifying the latent value of samples and employing a dynamic renovation strategy selection mechanism to determine the optimal component-level strategy. |
| Outcome: | The proposed framework achieves performance improvements using less than 10% of the data volume, underscoring that the latent potential of stale corpora remains largely untapped. |
Optimal Partial Transport Based Sentence Selection for Long-form Document Matching (2022.coling-1)
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| Challenge: | Existing methods for document matching are limited by the partial nature of the sentence-level matching signals. |
| Approach: | They propose a matching approach that equips existing document matching models with an Optimal Partial Transport component, namely OPT-Match, which selects the key sentences that play a major role in matching. |
| Outcome: | The proposed approach outperforms existing models on four publicly available datasets and the key sentences selected by it are consistent with human-provided rationales. |
KCVR: Knowledge-Centric Video Reconstruction for Structured Pedagogical Summarization via Dynamic Graph Planning (2026.acl-long)
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Jingjiang Liu, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Xiaokang Jin, Yilin Wang, Qingyu Niu, Jiawei Shen, Guoqing Ma, Yidan Liang, Shimin Di, Jiajie Xu
| Challenge: | Existing summarization methods compress content for gist browsing, but they break prerequisite logic in instructional videos. |
| Approach: | They propose a framework that decouples epistemic planning from content generation. |
| Outcome: | The proposed framework outperforms strong end-to-end baselines on Knowledge Progression Consistency and Learning Objective Coverage. |