Papers by Teng Liu
A Dialogue-based Information Extraction System for Medical Insurance Assessment (2021.findings-acl)
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Shuang Peng, Mengdi Zhou, Minghui Yang, Haitao Mi, Shaosheng Cao, Zujie Wen, Teng Xu, Hongbin Wang, Lei Liu
| Challenge: | a new system that integrates advanced NLP technologies for medical insurance assessment is proposed . the average time cost of the procedure is reduced from 55 minutes to 35 minutes . |
| Approach: | They propose a dialogue-based information extraction system that integrates advanced NLP technologies for medical insurance assessment. |
| Outcome: | The proposed system reduces the time cost of the procedure from 55 minutes to 35 minutes and saves 30% human resources cost compared with the previous offline procedure. |
A Mousetrap: Fooling Large Reasoning Models for Jailbreak with Chain of Iterative Chaos (2025.findings-acl)
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| Challenge: | Large Reasoning Models (LRMs) have advanced beyond traditional Large Language Models, yet they pose heightened safety risks. |
| Approach: | They propose a first jailbreak attack targeting Large Reasoning Models . they exploit a Chaos Machine component to transform attack prompts with diverse one-to-one mappings based on the reasoning chain . |
| Outcome: | The proposed attack exploits the unique vulnerabilities of LRMs by integrating a Chaos Machine. success rates of the mousetrap attack are as high as 96%, 86% and 98% respectively. |
Flames: Benchmarking Value Alignment of LLMs in Chinese (2024.naacl-long)
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Kexin Huang, Xiangyang Liu, Qianyu Guo, Tianxiang Sun, Jiawei Sun, Yaru Wang, Zeyang Zhou, Yixu Wang, Yan Teng, Xipeng Qiu, Yingchun Wang, Dahua Lin
| Challenge: | Existing benchmarks for large language models (LLMs) do not accurately uncover safety vulnerabilities in LLMs. |
| Approach: | They propose a value alignment benchmark called Flames that encompasses both harmlessness principles and a unique morality dimension that integrates specific Chinese values such as harmony. |
| Outcome: | The proposed model performs poorly on Flames, particularly in safety and fairness dimensions. |
USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS (2026.acl-long)
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Baolin Zheng, Guanlin Chen, Qingyang Teng, Hongqiong Zhong, Yingshui Tan, Zhendong Liu, Weixun Wang, Jiaheng Liu, Jian Yang, Huiyun Jing, Jincheng Wei, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang
| Challenge: | Existing safety benchmarks fail to provide reliable assessments due to limited risk coverage, insufficient scale and the oversight of complex modality combinations. |
| Approach: | They propose a framework that covers 61 risk categories across four modality interactions to address this gap. |
| Outcome: | The proposed framework covers 61 risk categories across four distinct modality interactions. |
Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs (2026.acl-long)
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Zhenyu Liu, Xuanyu Zhang, Yunxin li, Qixun Teng, Shenyuan Jiang, Haolan Chen, Mingjun Zhao, Fanbo Meng, Yu Xu, Yancheng He, Baotian Hu, Haizhou Li, Min Zhang
| Challenge: | despite significant progress, full-duplex SLMs are constrained by severe modality interference, authors say . modality interferes with acoustic and semantic modeling, making them unintelligent and unnatural . authors propose a hierarchical parameter separation strategy that decouples conflicting modalities in deep layers . |
| Approach: | They propose a hierarchical parameter separation strategy that decouples conflicting modalities in deep layers while preserving cross-modality coherence via a dedicated semantic alignment channel. |
| Outcome: | The proposed method significantly advances the state of the art on full-duplex benchmarks . it decouples conflicting modalities in deep layers while preserving cross-modality coherence . |
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation (2020.emnlp-main)
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| Challenge: | AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMRs) Graph Convolution Networks (GCNs) are not able to capture non-local information and follow a local (first-order) information aggregation scheme. |
| Approach: | They propose a dynamic fusion mechanism that captures richer non-local interactions . they propose weight tied convolutions and group graph convolution to reduce memory usage . |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets with significantly fewer parameters while maintaining the model capacity. |
BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving (2025.acl-long)
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Teng Wang, Wing Yin Yu, Zhenqi He, Zehua Liu, HaileiGong HaileiGong, Han Wu, Xiongwei Han, Wei Shi, Ruifeng She, Fangzhou Zhu, Tao Zhong
| Challenge: | Existing datasets in operations research domain lack detailed annotations of the modeling process, focusing only on objective values. |
| Approach: | They propose an annotation-based tree-of-thought tree-based reasoning algorithm that integrates reinforcement learning into a tree- of-though. |
| Outcome: | The proposed algorithm outperforms state-of-the-art methods on StructuredOR, NL4OPT, and MAMO-ComplexLP datasets. |
Harmonizing the Past, Present, and Future: A Null-Space Constrained Region-Specific Method for Continual Learning in LLMs (2026.acl-long)
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Jinhui Chen, Shizhu He, Xingchang Yang, Huanxuan Liao, Yequan Wang, Xiangwen Liao, Wenhao Teng, Kang Liu, Jun Zhao
| Challenge: | Existing continual learning paradigms prioritize instant performance through dense updates, leading to catastrophic forgetting and rapid exhaustion of model capacity. |
| Approach: | They propose a method that preserves previously acquired knowledge and acquires new task-specific skills while preserving sufficient parameter capacity for subsequent adaptation. |
| Outcome: | The proposed method is based on the brain's functional partitioning and can be used to map tasks between specialized and generalist neurons. |
DocAgent: A Multi-Agent System for Automated Code Documentation Generation (2025.acl-demo)
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| Challenge: | Existing methods for generating documentation using Large Language Models (LLMs) produce incomplete, unhelpful, or factually incorrect outputs. |
| Approach: | They propose a novel collaborative system that uses topological code processing for incremental context building to generate documentation by agents. |
| Outcome: | The proposed system outperforms baselines in completeness, helpfulness, and truthfulness evaluations. |
Exploring Self-supervised Logic-enhanced Training for Large Language Models (2024.naacl-long)
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| Challenge: | Traditional attempts to enhance the logical reasoning abilities of language models often rely on supervised fine-tuning, limiting their generalization to new tasks or domains. |
| Approach: | They propose a framework for integrating logical reasoning capabilities into LLMs and activating them via in-context learning. |
| Outcome: | The proposed framework achieves comparable results to existing models on three language understanding benchmarks. |
Beyond Task-Level Context: Class-Conditional Context Vectors for Implicit In-Context Learning (2026.findings-acl)
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| Challenge: | Existing approaches aggregate demonstrations from all classes into a shared, task-level context vector, capturing global task information but without explicitly preserving fine-grained, class-conditional semantic distinctions. |
| Approach: | They propose a class-conditional context vector extension to implicit in-context learning that explicitly models class-specific contextual information by constructing separate context vectors for each class. |
| Outcome: | The proposed extension outperforms task-level context vector baselines and achieves higher average accuracy than conventional few-shot learning on most models. |
Learning How to Remember: A Meta-Cognitive Management Method for Structured and Transferable Agent Memory (2026.findings-acl)
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| Challenge: | Existing approaches store memory in fixed representations and reuse it at a single or implicit level of abstraction, which limits generalization and often leads to negative transfer when distribution shift. |
| Approach: | They propose a Meta-Cognitive Memory Abstraction method which decouples task execution from memory management by combining a frozen task model with a learned memory copilot. |
| Outcome: | Experiments on ALFWorld, ScienceWorld, and BabyAI show that the proposed method improves performance, out-of-distribution generalization, and cross-task transfer over several baselines. |
UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity Mixture-of-Experts (2026.acl-long)
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Zhenyu Liu, Yunxin li, Xuanyu Zhang, Qixun Teng, Shenyuan Jiang, Xinyu Chen, Haoyuan Shi, Haolan Chen, Fanbo Meng, Mingjun Zhao, Yu Xu, Yancheng He, Baotian Hu, Haizhou Li, Min Zhang
| Challenge: | Recent advances in unified multimodal models indicate a clear trend towards comprehensive content generation. |
| Approach: | They propose a unified speech and music generation model built upon a novel framework . they propose specialized MoE architectures and curated training strategies to tackle data imbalances . |
| Outcome: | The proposed model achieves state-of-the-art performance on major speech and music generation benchmarks. |
MEBench: Benchmarking Large Language Models for Cross-Document Multi-Entity Question Answering (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) and Retrieval-augmented Generation (RAG) systems show promise, but their performance on cross-document MEQA remains underexplored due to the lack of tailored benchmarks. |
| Approach: | They propose a scalable multi-document, multi-entity benchmark to evaluate LLMs' capacity to retrieve, consolidate, and reason over scattered and dense information. |
| Outcome: | The proposed benchmarks show that even advanced models achieve only 59% accuracy on MEBench. |
How Well Do Text Embedding Models Understand Syntax? (2023.findings-emnlp)
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| Challenge: | Existing text embedding models have not addressed syntactic understanding challenges, highlighting ineffectiveness and enhancing generalization ability. |
| Approach: | They propose to examine the ability of text embedding models to generalize across syntactic contexts. |
| Outcome: | The proposed models exhibit high similarity socres at this simple task. |
Improving Knowledge Production Efficiency With Question Answering on Conversation (2023.acl-industry)
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| Challenge: | Existing researches on conversation-based QA focus on document-based tasks . current researche focuses on document based tasks, but there is a lack of researche on conversation based qa . |
| Approach: | They propose a multi-span extraction model on conversation-based QA and introduce continual pre-training and multi-task learning schemes to further improve model performance. |
| Outcome: | The proposed model outperforms baseline on two Chinese datasets and will be released for research purposes. |
ERCThinker: Fast-Slow Thinking for Emotion Recognition in Conversation (2026.acl-long)
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| Challenge: | Existing methods for ERC lack interpretability and shallow semantics capture deep semantics. |
| Approach: | They propose a Fast-Slow thinking framework for Emotion Recognition in Conversation . they use fine-grained emotion reasoning chains to capture deep semantics . |
| Outcome: | The proposed framework achieves state-of-the-art in explanation and judgment on a benchmark dataset. |
Uncertainty-Calibrated Elastic Alignment for Multimodal Sentiment Analysis with Missing Modalities (2026.findings-acl)
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Kang He, Yuzhe Ding, Rao Fu, Yukang Feng, Kaipeng Zhang, Yiming Liu, Fei Li, Chong Teng, Donghong Ji
| Challenge: | Existing methods for multimodal sentiment analysis are often dynamically incomplete. |
| Approach: | They propose a new uncertainty-calibrated elastic alignment framework to address these issues by employing probabilistic imputation to capture cross-modal ambiguity and leverage the estimated uncertainty to drive elastic alignment. |
| Outcome: | The proposed framework outperforms state-of-the-art models in multiple benchmarks and consistently outperformed existing models. |
Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs (2025.emnlp-main)
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Dayu Yang, Tianyang Liu, Daoan Zhang, Antoine Simoulin, Xiaoyi Liu, Yuwei Cao, Zhaopu Teng, Xin Qian, Grey Yang, Jiebo Luo, Julian McAuley
| Challenge: | Recent breakthrough models like OpenAI-o1 and DeepSeek-R1 show powerful task-solving capabilities, particularly advances in reasoning. |
| Approach: | They propose future research directions that may deepen the synergy, ultimately advancing LLM performance in both complex reasoning and code intelligence. |
| Outcome: | The proposed research may deepen the synergy, ultimately advancing LLM performance in both complex reasoning and code intelligence. |
Solving Aspect Category Sentiment Analysis as a Text Generation Task (2021.emnlp-main)
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| Challenge: | Existing methods for Aspect category sentiment analysis use pre-trained language models to learn aspect category-specific representations. |
| Approach: | They propose to make use of pre-trained language models by casting the ACSA tasks into natural language generation tasks, using natural language sentences to represent the output. |
| Outcome: | The proposed method gives the best reported results, having large advantages in few-shot and zero-shot settings. |
LogiCoT: Logical Chain-of-Thought Instruction Tuning (2023.findings-emnlp)
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| Challenge: | Recent work on self-instruction tuning has focused on enhancing the general proficiency of models. |
| Approach: | They propose a new instruction-tuning dataset for Logical Chain-of-Thought reasoning with GPT-4 that harvests instructions for prompting GPT to generate chain-of thought rationales. |
| Outcome: | The proposed dataset enables the model to generate chain-of-thought rationales with GPT-4. |
Learning Named Entity Tagger using Domain-Specific Dictionary (D18-1)
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| Challenge: | Existing methods to build reliable named entity recognition systems require large amounts of manually-labeled training data. |
| Approach: | They propose a revised fuzzy CRF layer to handle tokens with multiple possible labels to address noisy distant supervision. |
| Outcome: | The proposed model can handle tokens with multiple possible labels under the traditional framework and improves on the existing model with a new Tie or Break scheme. |
JoPR: Joint Emotion Perception and Reasoning for Conversational Emotion Recognition (2026.acl-long)
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| Challenge: | Existing methods for ERC lack human-like emotion reasoning and discrimination between similar emotions. |
| Approach: | They propose a multi-dimension curriculum with long CoT fine-tuning to clone human-like emotion reasoning for conversational emotion recognition. |
| Outcome: | The proposed model outperforms existing methods on three widely used datasets and shows that it is more intuitive and more accurate. |
ColorBrowserAgent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution (2026.acl-industry)
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Jihong Wang, Jiamu Zhou, Weiming Zhang, Teng Wang, Weiwen Liu, Zhuosheng Zhang, Xingyu Lou, Weinan Zhang, Huarong Deng, Jun Wang
| Challenge: | Xue et al., 2025): deploying autonomous web agents in production remains difficult due to site heterogeneity and long-horizon instability. |
| Approach: | They propose a knowledge-evolving agent that can be used to automate web workflows . they use human-in-the-loop knowledge adaptation and knowledge-aligned progressive summarization . |
| Outcome: | Experiments on WebArena, WebChoreAren and industrial deployment show it outperforms baselines. |
LANTERN in the Event Stream: Training-Free Temporal Knowledge Graph Forecasting by Balancing Inertia and Shifts (2026.findings-acl)
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| Challenge: | Temporal knowledge graph forecasting (TKGF) uses long-window strengthscores and short-windowed novelty scores to predict missing entities in future queries. |
| Approach: | They propose a training-freeprompting framework that uses two perspectives of history to predict missing entities in future queries. |
| Outcome: | The proposed framework outperforms the state-of-the-art baselineAnRe framework in ICEWS14, ICEW05-15, and GDELT. |
Spectral Disentanglement: Rank-Aware Task Adaptation for Rehearsal-free Continual Learning in LLMs (2026.acl-long)
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| Challenge: | Continual Learning (CL) for Large Language Models faces a fundamental Stability-Plasticity Dilemma . Rank-Blindness enforces a single rank constraint across diverse tasks, leading to catastrophic forgetting of earlier tasks and underfitting on complex new ones. |
| Approach: | They propose a rank-spectrum-based rehearsal-free framework that explicitly disentangles knowledge into two orthogonal subspaces. |
| Outcome: | The proposed framework achieves a superior stability-plasticity balance compared to single-rank baselines. |