Papers by Zhigang Wang
ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction (2026.findings-acl)
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Wenda Liu, Song Zhigang, Shuai Nie, Guangyao Liu, Lisung Chen, Binyu Yang, Yaran Chen, Peng Zhou, Hongzhen Wang, Yuchen Liu, Wenyue Hu, Jiaming Xu, Runyu Shi, Ying Huang
| Challenge: | ProUIE improves universal information extraction (UIE) without external information . many LLM-based methods rely on extra schema cues, external resources or complex alignment and verification pipelines . |
| Approach: | They propose a Macro-to-Micro progressive learning approach that improves UIE without external information. |
| Outcome: | ProUIE outperforms instruction-tuned baselines on average for NER and RE while using a smaller backbone. |
CCTC: A Cross-Sentence Chinese Text Correction Dataset for Native Speakers (2022.coling-1)
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| Challenge: | Chinese text correction datasets focus on detecting and correcting Chinese spelling errors and grammatical errors. |
| Approach: | They propose a Chinese text correction dataset for native speakers . they manually annotated 1,500 Chinese texts written by native speakers. |
| Outcome: | The proposed dataset can detect and correct Chinese spelling errors and grammatical errors. |
CINO: A Chinese Minority Pre-trained Language Model (2022.coling-1)
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| Challenge: | Existing multilingual pre-trained language models do not perform well on some low-resource languages. |
| Approach: | They propose a multilingual pre-trained language model for Chinese minority languages . they collect documents from Wikipedia and construct two classification datasets . |
| Outcome: | The proposed model outperforms baseline models on various classification tasks. |
HqeKV: Towards Hybrid Quantization and Eviction for KV Cache in Long-Context LLM Inference (2026.findings-acl)
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| Challenge: | autoregressive inference requires repeated computation across transformer layers. |
| Approach: | They propose a hybrid compression framework built on both quantization and eviction . they propose varying importance metric and flexible conversion policies to reduce memory overhead . |
| Outcome: | The proposed framework outperforms state-of-the-art methods under memory constraints. |
MusTQ: A Temporal Knowledge Graph Question Answering Dataset for Multi-Step Temporal Reasoning (2024.findings-acl)
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| Challenge: | Existing studies focus on fact-centered reasoning with limited attention to temporal reasoning. |
| Approach: | They propose a new TKGQA dataset, MusTQ, which contains 666K multi-step temporal reasoning questions and a TKG. |
| Outcome: | The proposed model achieves state-of-the-art multi-step temporal reasoning ability with entity-time attention mechanism and optimized temporal knowledge graph representation. |
Towards Efficient and Effective Diffusion Language Model Inference via Semantic-Aware Adaptive Denoising (2026.acl-long)
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| Challenge: | Existing acceleration works cannot accurately detect semantically stabilized tokens and then skip computation, leading to sub-optimal speedup in practice. |
| Approach: | They propose a semantic-aware adaptive denoising framework that encodes scalar confidence scores into an evolution-awful feature vector and clusters vectors proactively and adaptively identify semantically converged tokens. |
| Outcome: | The proposed framework outperforms the SOTA competitor in speed and quality . it can detect semantically stabilized tokens and skip computation, resulting in sub-optimal speedup . |