Papers by Shengping Liu
CroAno : A Crowd Annotation Platform for Improving Label Consistency of Chinese NER Dataset (2021.emnlp-demo)
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Baoli Zhang, Zhucong Li, Zhen Gan, Yubo Chen, Jing Wan, Kang Liu, Jun Zhao, Shengping Liu, Yafei Shi
| Challenge: | Existing crowd annotation tools for named entity recognition (NER) focus on efficiency and don't consider consistency of datasets. |
| Approach: | They propose a crowd annotation platform for Chinese named entity recognition (NER) CroAno provides a systematic solution for improving label consistency of Chinese NER datasets. |
| Outcome: | The proposed platform improves label consistency of Chinese NER datasets. |
GuideTree: Guideline-Induced Review Trees for Long Medical Records (2026.acl-industry)
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| Challenge: | Medical record reviewers must produce consistent, traceable, guideline-compliant outcomes . longcontext inference is expensive and often degrades as inputs grow . |
| Approach: | a new method compiles textual guidelines into a fixed review tree . a cost-aware split-and-prune search is used to update the tree offline . the algorithm produces consistent, traceable, guideline-compliant outcomes . |
| Outcome: | The proposed system outperforms the strongest non-expert baselines by 84.5–92.8 Macro-F1 . it reduces average I/O volume to 74K input+output characters and average latency to 22s . |
Transparentize the Internal and External Knowledge Utilization in LLMs with Trustworthy Citation (2025.findings-acl)
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| Challenge: | citation generation and retrieval-augmented generation are still lacking in large language models due to hallucinations. |
| Approach: | They propose a retrieval-augmented citation generation task that requires models to generate citations considering both external and internal knowledge while providing trustworthy references. |
| Outcome: | The proposed method achieves better performance across scenarios compared to baselines . retrieval quality, question types, and model knowledge influence trustworthiness . |
HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding (2020.acl-main)
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| Challenge: | Existing methods for ICD coding ignore Code Hierarchy and Code Co-occurrence . cost of manual coding estimated to be $25 billion per year in the US . |
| Approach: | They propose a hyperbolic representation method to leverage the code hierarchy and a graph convolutional network to utilize the code co-occurrence. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two widely used datasets. |
ZhuJiu: A Multi-dimensional, Multi-faceted Chinese Benchmark for Large Language Models (2023.emnlp-demo)
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Baoli Zhang, Haining Xie, Pengfan Du, Junhao Chen, Pengfei Cao, Yubo Chen, Shengping Liu, Kang Liu, Jun Zhao
| Challenge: | Various types of LLMs have recently been rapidly developing, such as Llama2 and ChatGLM2 . |
| Approach: | They propose a benchmark that comprehensively evaluates LLMs across 7 ability dimensions covering 51 tasks. |
| Outcome: | The proposed benchmarks are comprehensive and systematic, with a high level of accuracy and authority. |
Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering (2025.coling-main)
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| Challenge: | Recent studies indicate that Large Language Models model rich knowledge, but it is often not activated and awakened. |
| Approach: | They propose a framework that leverages richer context to enhance question answering . Explicit awakening fine-tunes a context generator to create a synthetic, compressed document that functions as symbolic context. |
| Outcome: | The proposed framework mimics the human ability to answer questions using only thinking and recalling to compensate for knowledge gaps. |
Clinical-Coder: Assigning Interpretable ICD-10 Codes to Chinese Clinical Notes (2020.acl-demos)
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| Challenge: | Existing methods of automatic coding prediction have been successful, but the interpretability of predicted codes is a challenge. |
| Approach: | They propose an online system that can predict ICD codes for Chinese clinical notes by using a Dilated Convolutional Attention network with N-gram Matching mechanism. |
| Outcome: | The proposed system is able to provide supporting information in clinical decision making. |
Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning (D19-1)
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| Challenge: | Existing works didn’t consider the extraction order of relational facts in a sentence. |
| Approach: | They propose to take the extraction order into consideration by applying reinforcement learning into a sequence-to-sequence model. |
| Outcome: | The proposed model could generate relational facts freely. |
Discerning and Resolving Knowledge Conflicts through Adaptive Decoding with Contextual Information-Entropy Constraint (2024.findings-acl)
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| Challenge: | Existing decoding methods for large language models (LLMs) are specialized in resolving knowledge conflicts and could inadvertently deteriorate performance in absence of conflicts. |
| Approach: | They propose an adaptive decoding method to discern whether knowledge conflicts occur and resolve them by a contextual information-entropy constraint decoding technique. |
| Outcome: | The proposed method improves the model’s faithfulness to conflicting context and maintains high performance among non-conflicting contexts. |
Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network (D19-1)
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| Challenge: | Existing models for named entity recognition (NER) lack word boundaries information, which is a major barrier to developing a high performance named entity system. |
| Approach: | They propose a Chinese named entity recognition system with word boundaries information . they use word-level representations and character-level models to integrate lexical knowledge into Chinese NER . |
| Outcome: | The proposed model outperforms the state-of-the-art model and achieves a speed of up to 15 times faster than the SOTA model. |
Beyond Modality Collapse: Taming Guided Modality Entropy for Omni-modal Emotion Reasoning (2026.findings-acl)
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| Challenge: | EmoOmni is a data paradigm for omni-modal large language models that can be used for emotion reasoning. |
| Approach: | They propose a data paradigm that interleaves guided tokens into reasoning traces to enforce structured evidence extraction. |
| Outcome: | The proposed paradigm over-relys on a dominant modality while neglecting complementary cues. |
VAPO: End-to-end Slide-Enhanced Speech Recognition with Omni-modal Large Language Models (2026.acl-long)
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| Challenge: | Current Automatic Speech Recognition models, such as Whisper, have demonstrated impressive performance in general domains, but their accuracy often deteriorates significantly in specialized scenarios. |
| Approach: | They propose a visually-anchored policy optimization approach to decouple visual perception from auditory processing to optimize the model's inference process. |
| Outcome: | The proposed model eliminates visual interference and achieves state-of-the-art performance on SlideASR-Bench and public datasets. |
FocalOrder: Focal Preference Optimization for Reading Order Detection (2026.acl-long)
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Fuyuan Liu, Dianyu Yu, He Ren, Nayu Liu, Xiaomian Kang, Delai Qiu, Fa Zhang, Genpeng Zhen, Shengping Liu, Liang Jiaen, null Weihuang, Yining Wang, Junnan Zhu
| Challenge: | Existing methods for document comprehension rely on uniform supervision, resulting in a performance degradation in the intermediate sections. |
| Approach: | They propose a framework driven by Focal Preference Optimization to detect reading order in document layouts. |
| Outcome: | The proposed framework outperforms competing baselines and surpasses large-scale general VLMs. |
Biomedical Concept Normalization by Leveraging Hypernyms (2021.emnlp-main)
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| Challenge: | Biomedical Concept Normalization (BCN) is widely used in biomedical text processing . despite numerous surface variants of biomedically-defined concepts, it remains challenging and unsolved. |
| Approach: | They propose a framework that uses hypernyms and synonyms to facilitate BCN . they use list-wise training to make use of both hypernies and synonym entities . |
| Outcome: | The proposed framework outperforms the state-of-the-art model on the NCBI dataset. |
MIE: A Medical Information Extractor towards Medical Dialogues (2020.acl-main)
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| Challenge: | EMRs are important but many doctors suffer from writing them, which is time-consuming and tedious. |
| Approach: | They propose an automatic conversion of medical dialogues to EMRs using a window-sliding style . they propose a medical information extractor (MIE) that extracts medical information from medical dialogue . |
| Outcome: | The proposed model extracts medical information from doctor-patient dialogues. |
Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism (2021.acl-long)
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| Challenge: | Existing methods for ICD coding ignore the long-tail of code frequency or noisy clinical notes. |
| Approach: | They propose to use an interactive shared representation network to model code co-occurrences while focusing on the clinical note's noteworthy part and extract valuable information through a self-distillation learning mechanism to solve the long-tail problem. |
| Outcome: | The proposed model reduces the long-tail of code frequency and noise in clinical notes and extracts valuable information through a self-distillation learning mechanism. |
Cracking Factual Knowledge: A Comprehensive Analysis of Degenerate Knowledge Neurons in Large Language Models (2025.acl-long)
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| Challenge: | Large language models (LLMs) are believed to store extensive factual knowledge, yet the mechanisms of knowledge storage in LLMs remain largely unexplored. |
| Approach: | They propose that some multi-layer perceptron neurons can store "knowledge". |
| Outcome: | The proposed model can store "knowledge" in multi-layer perceptron neurons, but not redundancy. |
High-Quality Medical Dialogue Synthesis for Improving EMR Generation (2025.emnlp-industry)
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| Challenge: | Existing methods for generating EMRs from doctor-patient dialogues produce rigid and repetitive dialogues. |
| Approach: | They propose a framework that integrates Intent Graph Planning, Dual-Agent Simulation and Rule-Reward Quality Control to generate realistic doctor-patient dialogues. |
| Outcome: | The proposed framework significantly enhances realism, diversity and downstream EMR quality, reducing physician editing efforts. |
Investigating and Enhancing Vision-Audio Capability in Omnimodal Large Language Models (2025.findings-acl)
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| Challenge: | Recent years have witnessed significant advancements in large language models (LLMs) but still struggle with integrating vision and audio. |
| Approach: | They propose a self-knowledge distillation method to improve vision-audio capabilities of OLLMs by learning from the vision-text components. |
| Outcome: | The proposed method improves vision-audio capabilities of OLLMs by learning from vision-text components, which improves interaction between audio and images and results in improved performance on multimodal tasks. |
Large Language Models are Better Reasoners with Self-Verification (2023.findings-emnlp)
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| Challenge: | Existing methods to solve complex natural language processing tasks require multiple steps to verify the answers. |
| Approach: | They propose to use chain of thought prompting to solve reasoning tasks with large language models. |
| Outcome: | The proposed method can improve reasoning performance on arithmetic, commonsense, and logical reasoning datasets. |
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism (D18-1)
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| Challenge: | Existing methods for named entity recognition (NER) do not exploit word boundary information from CWS or cannot filter the specific information of CWS. |
| Approach: | They propose to exploit task-shared boundary information to make full use of Chinese NER task and Chinese word segmentation (CWS) . |
| Outcome: | The proposed model significantly outperforms other state-of-the-art methods on two widely used datasets. |