Papers by Tao Zou
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective (2022.acl-long)
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Xiao Wang, Shihan Dou, Limao Xiong, Yicheng Zou, Qi Zhang, Tao Gui, Liang Qiao, Zhanzhan Cheng, Xuanjing Huang
| Challenge: | Named Entity Recognition models are feature-engineering and machine learning based. |
| Approach: | They propose a new NER learning framework that uses entity mentions to improve model performance. |
| Outcome: | The proposed model achieves better performance on OOV entities on various settings and datasets. |
A Lexicon-Based Graph Neural Network for Chinese NER (D19-1)
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| Challenge: | Chinese named entity recognition models are vulnerable to word ambiguities due to the lack of global semantics and chain structure. |
| Approach: | They propose a lexicon-based graph neural network with global semantics to solve word ambiguities in Chinese named entity recognition (NER) Lexicons are used to construct the graph and provide word-level features. |
| Outcome: | The proposed model improves on four NER datasets on Chinese characters, potential words, and the whole-sentence semantics. |
Reasoning-Augmented Conversation for Multi-Turn Jailbreak Attacks on Large Language Models (2025.findings-emnlp)
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Zonghao Ying, Deyue Zhang, Zonglei Jing, Yisong Xiao, Quanchen Zou, Aishan Liu, Siyuan Liang, Xiangzheng Zhang, Xianglong Liu, Dacheng Tao
| Challenge: | Existing methods for implementing multi-turn jailbreaks struggle to balance semantic coherence with attack effectiveness, resulting in benign semantic drift or ineffective detection evasion. |
| Approach: | They propose a framework that reformulates harmful queries into benign reasoning tasks and leverages LLMs’ strong reasoning capabilities to compromise safety alignment. |
| Outcome: | The proposed framework achieves state-of-the-art attack effectiveness in complex conversational scenarios, with average ASRs increasing by up to 96%. |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)
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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen
| Challenge: | Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed . |
| Approach: | They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities. |
| Outcome: | The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech . |
Towards Understanding Omission in Dialogue Summarization (2023.acl-long)
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| Challenge: | Existing methods for dialogue summarization are far from satisfactory . omission is a major factor in affecting the quality of summarizing, but few studies have explored the problem . |
| Approach: | They propose a dataset that provides high-quality omission labels for dialogue summarization . they propose to use this dataset to detect omitted dialogue utterances . |
| Outcome: | The proposed dataset improves summarization quality by providing ground-truth omission labels . the proposed dataset and codes are publicly available . |
Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks (2022.coling-1)
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Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu
| Challenge: | Large pre-trained language models (PLMs) have demonstrated superior performance in industrial applications. |
| Approach: | They propose a framework that re-uses existing parameter-efficient methods with a unified classifier. |
| Outcome: | The proposed framework improves the efficiency of existing parameter-efficient methods with a unified classifier. |
Improved Pseudo Data for Machine Translation Quality Estimation with Constrained Beam Search (2023.emnlp-main)
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Xiang Geng, Yu Zhang, Zhejian Lai, Shuaijie She, Wei Zou, Shimin Tao, Hao Yang, Jiajun Chen, Shujian Huang
| Challenge: | evaluating the quality of machine translation outputs becomes increasingly essential with the rapid development of machine language (MT). |
| Approach: | They propose to generate pseudo data using the MT model with constrained beam search (CBSQE) they propose to preserve the reference parts with high MT probabilities as correct translations . |
| Outcome: | The proposed model outperforms strong baselines in both supervised and unsupervised settings. |
OEE-CFC: A Dataset for Open Event Extraction from Chinese Financial Commentary (2024.findings-emnlp)
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Qizhi Wan, Changxuan Wan, Rong Hu, Dexi Liu, Xu Wenwu, Kang Xu, Zou Meihua, Liu Tao, Jie Yang, Zhenwei Xiong
| Challenge: | Existing corpora with unconventional entities serving as event arguments lack rich multi-events and shared arguments. |
| Approach: | They develop an open event template that includes 21 event argument roles and an open corpus supporting open event extraction. |
| Outcome: | The proposed corpus includes 17,469 events, 44,221 arguments, 3,644 complex arguments, and 5,898 shared arguments. |
Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining (2021.emnlp-main)
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| Challenge: | Existing methods for low-resource dialogue summarization neglect the difference between dialogues and conventional articles. |
| Approach: | They propose a multi-source pretraining paradigm to leverage external summary data . they exploit large-scale in-domain non-summary data to separate dialogue encoder and summary decoder . |
| Outcome: | The proposed model can be used to better leverage external summary data. |
PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning (2024.findings-emnlp)
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| Challenge: | Recent advances in fine-tuning large language models have greatly enhanced their usage in domain-specific tasks. |
| Approach: | They propose a method which internalizes prompt knowledge during model fine-tuning to achieve efficient inference and save costs. |
| Outcome: | The proposed approach reduces input tokens by 90%, accelerates inference by 4.2 times, and reduces monetary inference costs by 88.3%. |
Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment (2026.acl-long)
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Yuming Yang, Mingyoung Lai, Wanxu Zhao, Xiaoran Fan, Zhiheng Xi, Mingqi Wu, Chiyue Huang, Jun Zhao, Haijun Lv, Jian Tong, Yunhua Zhou, Yicheng Zou, Qipeng Guo, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing methods assess suitability primarily through student likelihood, favoring trajectories that align closely with the student model’s current behavior but overlooking more informative ones. |
| Approach: | They propose a Rank–Surprisal Ratio metric that captures both alignment and informativeness to assess the suitability of a reasoning trajectory. |
| Outcome: | The proposed metric captures both alignment and informativeness to assess the suitability of a reasoning trajectory. |
EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language Models (2025.emnlp-main)
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| Challenge: | Existing benchmarks focusing on single-task environments with limited constraints lack the complexity required to fully reflect the evolution of large language models (LLMs). |
| Approach: | They propose to use a Segment Policy Optimization algorithm to enhance the LLM's ability to accurately fulfill multi-task workflows. |
| Outcome: | The proposed benchmarks show that existing benchmarks lack the complexity required to fully reflect the evolution of large language models. |
Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models (2026.acl-long)
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| Challenge: | Semi-autoregressive (Semi-AR) decoding suffers from inherent block constraints . naive lookahead decoding is unreliable, token stability closely correlates with convergence trend, and historical information is isolated. |
| Approach: | They propose a training-free, plug-and-play dynamic decoding strategy that monitors the stability of tokens in real time through dynamic anchors. |
| Outcome: | The proposed approach reduces decoding steps by 80% while improving performance by 3.67% on the BBH benchmark. |
Pretraining Language Models with Text-Attributed Heterogeneous Graphs (2023.findings-emnlp)
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| Challenge: | Existing pretraining tasks for Language Models (LMs) focus on learning the textual information of each entity and overlook the crucial aspect of capturing topological connections among entities in TAHGs. |
| Approach: | They propose a topology-aware pretraining task that explicitly considers the topological and heterogeneous information in TAHGs by optimizing an LM and an auxiliary heterogenous graph neural network. |
| Outcome: | The proposed framework explicitly considers the topological and heterogeneous information in TAHGs. |
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents (2022.findings-acl)
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| Challenge: | Existing text semantic matching models do not provide granularity for text comparison. |
| Approach: | They propose a simple yet effective training strategy for text semantic matching by disentangling keywords from intents. |
| Outcome: | The proposed approach achieves stable performance improvements against a wide range of models on three benchmarks. |
A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis (C18-1)
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| Challenge: | Existing attention models do not take full advantage of sentiment lexicons, which provide rich sentiment information and play a critical role in sentiment analysis. |
| Approach: | They propose a lexicon-based supervised attention model which allows a neural network to focus on the sentiment content, thus generating sentiment-informative representations. |
| Outcome: | The proposed model outperforms existing models on three large-scale sentiment classification datasets. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |