Papers by Xiaozhi Wang

OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding (2023.emnlp-demo)

Challenge:	Prior research on linguistic mechanisms of large language models is limited by coarse granularity, limited analysis scale, and narrow focus.
Approach:	They propose a framework for analyzing the linguistic mechanisms of large language models based on Sparse Auto-Encoders.
Outcome:	The proposed framework extracts Chinese and English linguistic features across four dimensions . it uncovers intrinsic representations of linguistic knowledge in LLMs and can control outputs .

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Hao Peng, Xiaozhi Wang, Feng Yao, Zimu Wang, Chuzhao Zhu, Kaisheng Zeng, Lei Hou, Juanzi Li

MAVEN: A Massive General Domain Event Detection Dataset (2020.emnlp-main)

Challenge:	Event understanding is fundamental for humans to understand the world.
Approach:	They propose an event understanding toolkit called OmniEvent that is comprehensive and fair . it supports mainstream modeling paradigms and the processing of 15 widely-used datasets .
Outcome:	The toolkit supports mainstream modeling paradigms and the processing of 15 widely-used English and Chinese datasets.

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Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou

MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation (2024.acl-long)

Challenge:	Existing datasets exhibit data scarcity and limited coverage of general-domain events.
Approach:	They present a MAssive eVENt detection dataset which contains 4,480 Wikipedia documents and 168 event types.
Outcome:	The proposed dataset shows that existing methods cannot achieve promising results on the small datasets.

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Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie Zhou, Juanzi Li

A Self-supervised Joint Training Framework for Document Reranking (2022.findings-naacl)

Challenge:	Existing datasets for event understanding have limited coverage due to complexity of tasks.
Approach:	They propose a dataset that augments MAVEN datasets with event argument annotations . they propose 98,591 events and 290,613 arguments obtained with laborious human annotation .
Outcome:	The proposed dataset is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction.

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Xiaozhi Zhu, Tianyong Hao, Sijie Cheng, Fu Lee Wang, Hai Liu

COPEN: Probing Conceptual Knowledge in Pre-trained Language Models (2022.emnlp-main)

Challenge:	Pretrained language models have been successfully applied to a wide range of tasks . however, the pretraining tasks were based on the context of documents .
Approach:	They propose a self-supervised joint training framework with a method called Masked Query Prediction to establish semantic relations between given queries and positive documents.
Outcome:	The proposed framework outperforms existing models on document reranking tasks without further pre-training . it uses a self-supervised method to establish semantic relations between given queries and positive documents.

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Hao Peng, Xiaozhi Wang, Shengding Hu, Hailong Jin, Lei Hou, Juanzi Li, Zhiyuan Liu, Qun Liu

Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction (2023.emnlp-main)

Challenge:	Existing knowledge probing studies focus on evaluating factual knowledge of pre-trained language models (PLMs) but ignore conceptual knowledge.
Approach:	They evaluate conceptual knowledge of pre-trained language models by annotating 24k data instances covering 393 concepts.
Outcome:	The proposed tasks evaluate pre-trained language models' conceptual knowledge of entities, learn conceptual properties, and conceptualize entities in contexts.

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Ji Qi, Chuchun Zhang, Xiaozhi Wang, Kaisheng Zeng, Jifan Yu, Jinxin Liu, Lei Hou, Juanzi Li, Xu Bin

Adversarial Training for Weakly Supervised Event Detection (N19-1)

Challenge:	Existing evaluation benchmarks focus on pairwise matching, ignoring robustness . current models exhibit frustrating degradation, with a maximum drop of 23.43 F1 score .
Approach:	They propose a benchmark that simulates the evaluation of open information extraction models in the real world . they perform experiments on typical models published in the last decade and a representative large language model .
Outcome:	The proposed model is rated robust on a knowledge-invariant clique with different syntactic and expressive forms.

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Xiaozhi Wang, Xu Han, Zhiyuan Liu, Maosong Sun, Peng Li

KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation (2021.tacl-1)

Challenge:	Detecting and identifying events is an important subtask of event extraction.
Approach:	They build a large event-related candidate set with good coverage and apply an adversarial training mechanism to iteratively identify informative instances from the candidate set and filter out those noisy ones.
Outcome:	The proposed method significantly outperforms the state-of-the-art methods on two real-world datasets.

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Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li, Jian Tang

WildReward: Learning Reward Models from In-the-Wild Human Interactions (2026.acl-long)

Challenge:	Existing language representation models (PLMs) cannot capture factual knowledge from text.
Approach:	They propose a unified model for Knowledge Embedding and Pre-trained LanguagERepresentation which integrates factual knowledge into PLMs and produces effective text-enhanced KE with the strong PLM.
Outcome:	The proposed model improves on existing pre-trained language representation models and improves their performance on various NLP tasks.

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Hao Peng, Yunjia Qi, Xiaozhi Wang, Zijun Yao, Lei Hou, Juanzi Li

Challenge:	Prior work focused on collecting preference pairs, requiring substantial annotation efforts.
Approach:	They propose a pipeline to extract reliable human feedback from in-the-wild interactions . they propose to use WildChat as an interaction source to train the model .
Outcome:	The proposed model achieves comparable or even superior performance compared to conventional models with improved calibration and cross-sample consistency.

Neural Gibbs Sampling for Joint Event Argument Extraction (2020.aacl-main)

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Xiaozhi Wang, Shengyu Jia, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Jie Zhou

Sub-Character Tokenization for Chinese Pretrained Language Models (2023.tacl-1)

Challenge:	Existing methods for event argument extraction cannot adequately model the correlation between event arguments and their roles.
Approach:	They propose a Bayesian model to jointly extract event arguments using Gibbs sampling . they train two neural networks to model prior distribution and conditional distribution over event arguments .
Outcome:	The proposed model can achieve comparable results to existing methods on two widely-used datasets.

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Chenglei Si, Zhengyan Zhang, Yingfa Chen, Fanchao Qi, Xiaozhi Wang, Zhiyuan Liu, Yasheng Wang, Qun Liu, Maosong Sun

LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks (2025.acl-long)

Challenge:	Existing tokenization methods for Chinese PLMs treat each character as an indivisible token, but ignore the unique feature of the writing system where additional linguistic information exists below the character level.
Approach:	They propose to encode Chinese characters into short sequences and construct Chinese vocabulary based on the encoded text.
Outcome:	The proposed tokenizers can tokenize inputs into much shorter sequences, improving computational efficiency.

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Yushi Bai, Shangqing Tu, Jiajie Zhang, Hao Peng, Xiaozhi Wang, Xin Lv, Shulin Cao, Jiazheng Xu, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li

Reasoning

Challenge:	Existing benchmarks on longcontext large language models fail to reflect their deep understanding capabilities across diverse tasks.
Approach:	They propose a benchmark to assess the ability of long-context large language models to handle long-text problems.
Outcome:	The proposed model achieves 50.1% accuracy when directly answering the questions . human experts achieve only 53.7% accuracy under a 15-minute time constraint .

READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises (2023.acl-long)

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Chenglei Si, Zhengyan Zhang, Yingfa Chen, Xiaozhi Wang, Zhiyuan Liu, Maosong Sun

Emergent Modularity in Pre-trained Transformers (2023.findings-acl)

Challenge:	Existing benchmarks for Chinese inputs often lack a realistic representation of real-world noises.
Approach:	They construct a Chinese multi-task benchmark with REalistic and Diverse input noises . they use pinyin input and speech input to recruit speakers from diverse dialects based on their inputs - a feature that is important for Chinese NLP benchmarks if it is implemented in real-world applications.
Outcome:	The proposed benchmarks are based on four different tasks and are designed to maximize diversity.

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Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Chaojun Xiao, Xiaozhi Wang, Xu Han, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Jie Zhou

CLEVE: Contrastive Pre-training for Event Extraction (2021.acl-long)

Challenge:	Existing studies on pre-trained Transformers show that they learn fine-grained neuron functions.
Approach:	They examine the presence of modularity in pre-trained Transformers . they focus on Mixture-of-Experts, a promising candidate for modularity .
Outcome:	The proposed structure stabilizes at the early stage, which is faster than neuron stabilization.

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Ziqi Wang, Xiaozhi Wang, Xu Han, Yankai Lin, Lei Hou, Zhiyuan Liu, Peng Li, Juanzi Li, Jie Zhou

Adversarial Multi-lingual Neural Relation Extraction (C18-1)

Challenge:	Existing EE methods do not model event characteristics from large unsupervised data.
Approach:	They propose a contrastive pre-training framework for event extraction to better learn event knowledge from large unsupervised data and their semantic structures.
Outcome:	The proposed framework improves on ACE 2005 and MAVEN datasets on event extraction tasks.

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Xiaozhi Wang, Xu Han, Yankai Lin, Zhiyuan Liu, Maosong Sun

VerIF: Verification Engineering for Reinforcement Learning in Instruction Following (2025.emnlp-main)

Challenge:	Existing models cannot capture consistency and diversity of relation patterns in different languages.
Approach:	They propose an adversarial multi-lingual neural relation extraction model which considers consistency and diversity among languages.
Outcome:	The proposed model outperforms the state-of-the-art models on real-world datasets.

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Hao Peng, Yunjia Qi, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li

Challenge:	Best practices for RL in instruction following remain underexplored.
Approach:	They propose a verification method that combines rule-based code verification with LLM-based verification from a large reasoning model.
Outcome:	The proposed method achieves state-of-the-art performance among models of comparable size and generalizes well to unseen constraints.

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction (2022.emnlp-main)

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Xiaozhi Wang, Yulin Chen, Ning Ding, Hao Peng, Zimu Wang, Yankai Lin, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou

Train No Evil: Selective Masking for Task-Guided Pre-Training (2020.emnlp-main)

Challenge:	Existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions.
Approach:	They construct a large-scale human-annotated ERE dataset with improved annotation schemes to address these drawbacks.
Outcome:	The proposed dataset is larger than existing datasets of all the ERE tasks by at least an order of magnitude.

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Yuxian Gu, Zhengyan Zhang, Xiaozhi Wang, Zhiyuan Liu, Maosong Sun

TacoERE: Cluster-aware Compression for Event Relation Extraction (2024.lrec-main)

Challenge:	Pre-trained language models can't capture domain-specific and task-specific patterns because of the task-agnostic pre-training stage.
Approach:	They propose a task-guided pre-training stage with selective masking between general pre-train and fine-tuning to learn domain-specific patterns.
Outcome:	The proposed method can achieve comparable or even better performance with less than 50% of computation cost.

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Yong Guan, Xiaozhi Wang, Lei Hou, Juanzi Li, Jeff Z. Pan, Jiaoyan Chen, Freddy Lecue

ADELIE: Aligning Large Language Models on Information Extraction (2024.emnlp-main)

Challenge:	Existing work on event relation extraction focuses on modeling the entire document . existing methods cannot handle long-range dependencies and information redundancy .
Approach:	They propose a compression-then-extraction paradigm for event relation extraction . they propose document clustering for modeling event dependencies and then a cluster summarization method .
Outcome:	The proposed method simplifies and highlights important text content of clusters for mitigating redundancy and event distance.

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Yunjia Qi, Hao Peng, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li

Finding and Editing Multi-Modal Neurons in Pre-Trained Transformers (2024.findings-acl)

Challenge:	Large language models (LLMs) struggle to follow complex instructions of IE tasks due to not being aligned with humans.
Approach:	They propose an aligned large language moDEL that effectively solves various IE tasks including closed IE, open IE and on-demand IE.
Outcome:	The proposed model achieves state-of-the-art (SoTA) performance among open-source models.

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Haowen Pan, Yixin Cao, Xiaozhi Wang, Xun Yang, Meng Wang

On Transferability of Prompt Tuning for Natural Language Processing (2022.naacl-main)

Challenge:	Existing methods to identify key neurons for interpretability of multi-modal large language models are unclear.
Approach:	They propose a method to identify key neurons for interpretability by multi-modal large language models.
Outcome:	The proposed method improves conventional works upon efficiency and applied range by removing needs of costly gradient computation.

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Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie Zhou

The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation (2023.findings-acl)

Challenge:	Pre-trained language models (PLMs) can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but require much more training time than fine-timing.
Approach:	They empirically investigate the transferability of soft prompts across different downstream tasks and PLMs to determine what decides prompt transferability.
Outcome:	The proposed method can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but requires much more training time than fine-timing.

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Hao Peng, Xiaozhi Wang, Feng Yao, Kaisheng Zeng, Lei Hou, Juanzi Li, Zhiyuan Liu, Weixing Shen

Finding Skill Neurons in Pre-trained Transformer-based Language Models (2022.emnlp-main)

Challenge:	Event extraction (EE) is a fundamental information extraction task aimed at extracting events from plain texts.
Approach:	They propose to specify data preprocessing, standardize outputs, and provide pipeline evaluation results to avoid these pitfalls.
Outcome:	The results show that the evaluations are reliable and lack pipeline evaluations.

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Xiaozhi Wang, Kaiyue Wen, Zhengyan Zhang, Lei Hou, Zhiyuan Liu, Juanzi Li

LEVEN: A Large-Scale Chinese Legal Event Detection Dataset (2022.findings-acl)

Challenge:	Pre-trained language models have demonstrated superior performance on various natural language processing tasks.
Approach:	They find that after prompt tuning, some neurons encode task-specific skills . they also show that skill neurons are most likely generated in pre-training .
Outcome:	The neurons are highly predictive of task labels after prompt tuning for specific tasks.

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Feng Yao, Chaojun Xiao, Xiaozhi Wang, Zhiyuan Liu, Lei Hou, Cunchao Tu, Juanzi Li, Yun Liu, Weixing Shen, Maosong Sun

HMEAE: Hierarchical Modular Event Argument Extraction (D19-1)

Challenge:	Existing legal event detection datasets only cover incomprehensive event types and have limited annotated data.
Approach:	They present a large-scale Chinese legal event detection dataset . they use legal events as side information to promote downstream applications .
Outcome:	The proposed method improves 2.2 points precision in low-resource judgment prediction and 1.5 points precision for unsupervised case retrieval.

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Xiaozhi Wang, Ziqi Wang, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Maosong Sun, Jie Zhou, Xiang Ren

MAVEN-FACT: A Large-scale Event Factuality Detection Dataset (2024.findings-emnlp)

Challenge:	Existing event extraction methods classify each argument role independently, ignoring conceptual correlations between different argument roles.
Approach:	They propose a Hierarchical Modular Event Argument Extraction model to provide inductive bias from the concept hierarchy of event argument roles.
Outcome:	The proposed model outperforms existing methods on real-world datasets and shows that it leverages useful knowledge from the concept hierarchy.

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Chunyang Li, Hao Peng, Xiaozhi Wang, Yunjia Qi, Lei Hou, Bin Xu, Juanzi Li