Papers by Hao Lei

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

Challenge:	Pre-trained contextual representations like BERT have been widely used for NLP tasks.
Approach:	They propose to transform anisotropic sentence embedding distribution to smooth and isotropic Gaussian distribution by normalizing flows that are learned with an unsupervised objective.
Outcome:	The proposed method achieves significant performance gains over state-of-the-art embeddings on a variety of semantic textual similarity tasks.

Copied to clipboard

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

Discreteness in Neural Natural Language Processing (D19-2)

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.

Copied to clipboard

Lili Mou, Hao Zhou, Lei Li

Challenge:	This tutorial provides a comprehensive guide to the process of discreteness in neural NLP.
Approach:	This tutorial provides a comprehensive guide to the process of discreteness in neural NLP.
Outcome:	This tutorial explains the process of discreteness in neural NLP.

Generating Fluent Adversarial Examples for Natural Languages (P19-1)

Copied to clipboard

Huangzhao Zhang, Hao Zhou, Ning Miao, Lei Li

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

Challenge:	Current methods for building adversarial attackers for NLP are inefficient as the gradient is discarded.
Approach:	They propose an adversarial attacker which performs Metropolis-Hastings sampling with the guidance of gradients to solve these problems.
Outcome:	The proposed algorithm outperforms the baseline model on attacking capability on IMDB and SNLI.

Copied to clipboard

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

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

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.

Copied to clipboard

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

Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training (2026.acl-long)

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.

Copied to clipboard

Lei Liu, Hao Zhu, Xiaoyan Yang, Yue Shen, Zhixuan Chu, Jian Wang, Jinjie Gu, Kui Ren

ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors (2024.findings-emnlp)

Challenge:	Large language models (LLMs) have impressive capabilities across a wide range of domains, but their generalpurpose pre-training objectives often leave them illsuited for specialized applications such as healthcare.
Approach:	They propose a perplexity-aware data scaling law that establishes a predictive relationship between the perplexities of domain-specific data and the test loss.
Outcome:	Experiments on medical and general-domain benchmarks show that the proposed scaling law consistently identifies near-optimal training subsets with significantly reduced data consumption.

Copied to clipboard

Zhexin Zhang, Yida Lu, Jingyuan Ma, Di Zhang, Rui Li, Pei Ke, Hao Sun, Lei Sha, Zhifang Sui, Hongning Wang, Minlie Huang

Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training (2026.acl-long)

Challenge:	Existing tools for detecting safety issues in LLMs are expensive and inefficient.
Approach:	They propose an LLM-based safety detector which annotates the safety of queries and provides explanations for its decisions.
Outcome:	The proposed detector outperforms baselines on four sets of query-response pairs and is effective as a safety evaluator for advanced LLMs.

Copied to clipboard

Kailai Yang, Xiao Liu, Lei Ji, Hao Li, Xiao Liang, Zhiwei Liu, Yeyun Gong, Peng Cheng, Mao Yang

BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook (2026.acl-long)

Challenge:	Existing methods for reweighting data mixtures rely on manual designation with certain heuristics based on intuition or empirical results.
Approach:	They propose a model-based framework that learns to re-weight domains by reinforcement learning on large quantities of data mixing trajectories with corresponding feedback from an evaluation environment.
Outcome:	The proposed framework outperforms baselines in achieving balanced performance across source and target fields and domain spaces without retraining.

Copied to clipboard

Hao Gu, Lujun Li, Hao Wang, Lei Wang, Zheyu Wang, Bei Liu, Jiacheng Liu, Qiyuan Zhu, Sirui Han, Yike Guo

Text Clustering

Challenge:	Recent sparsity-aware binarization approaches can achieve sub-1-bit compression, but they face performance degradation, mask-management overhead, and limited hardware compatibility.
Approach:	They propose a binary quantization framework that leverages binary pattern clustering and weight transformation to overcome performance degradation and mask-management overhead.
Outcome:	The proposed framework achieves state-of-the-art compression (1.11–0.7 bits) it maintains high performance with only a 3.1% accuracy drop in zero-shot benchmarks while delivering a 1.6 speedup over FP16.

On Tree-Based Neural Sentence Modeling (D18-1)

Copied to clipboard

Haoyue Shi, Hao Zhou, Jiaze Chen, Lei Li

Syntactic Text Processing

Challenge:	Existing tree-based sentence modeling approaches adopt syntactic parsing trees as the explicit structure prior.
Approach:	They replace parsing trees with trivial trees to study their effectiveness . they found that tree-based sentence modeling gives better results when crucial words are closer to the final representation .
Outcome:	The proposed tree-based sentences have shown better results on many downstream tasks.

Xiaomingbot: A Multilingual Robot News Reporter (2020.acl-demos)

Copied to clipboard

Runxin Xu, Jun Cao, Mingxuan Wang, Jiaze Chen, Hao Zhou, Ying Zeng, Yuping Wang, Li Chen, Xiang Yin, Xijin Zhang, Songcheng Jiang, Yuxuan Wang, Lei Li

UniRE: A Unified Label Space for Entity Relation Extraction (2021.acl-long)

Challenge:	Xiaomingbot is a multilingual and multimodal software robot with four capabilities: news generation, news translation, news reading and avatar animation.
Approach:	They propose to build a multilingual and multimodal software robot with four inte- gal capabilities: news generation, news translation, news reading and avatar animation.
Outcome:	The proposed system generates Chinese news, then reads it in multiple languages and generates an animated avatar reading it.

Copied to clipboard

Yijun Wang, Changzhi Sun, Yuanbin Wu, Hao Zhou, Lei Li, Junchi Yan

Generating Sentences from Disentangled Syntactic and Semantic Spaces (P19-1)

Challenge:	Existing joint entity relation extraction models setup two separate label spaces for the two sub-tasks .
Approach:	They propose to eliminate the different treatment on the two sub-tasks’ label spaces by applying a unified classifier to predict each cell’s label.
Outcome:	The proposed model achieves competitive accuracy with the best extractor and is faster.

Copied to clipboard

Yu Bao, Hao Zhou, Shujian Huang, Lei Li, Lili Mou, Olga Vechtomova, Xin-yu Dai, Jiajun Chen

Syntactic Text Processing

Challenge:	Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space.
Approach:	They propose to generate sentences from disentangled syntactic and semantic spaces by using the linearized tree sequence.
Outcome:	The proposed method achieves similar or better performance in various tasks compared with state-of-the-art models.

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

Copied to clipboard

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.

E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning (2022.findings-acl)

Copied to clipboard

Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou

Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation (2025.emnlp-main)

Challenge:	Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models.
Approach:	They propose an explanation benchmark for analogical reasoning using a Civil Service exam . they use a free-text explanation scheme to explain whether an analogy should be drawn .
Outcome:	The proposed benchmark is very challenging for state-of-the-art models, it is found.

Copied to clipboard

Kun Peng, Cong Cao, Hao Peng, Guanlin Wu, Zhifeng Hao, Lei Jiang, Yanbing Liu, Philip S. Yu

Sentiment Analysis

Challenge:	Existing research on emotion recognition in conversation does not reach a consensus on classification theories . despite this, there is no clear consensus on how to recognize previously unseen emotions in real-world applications.
Approach:	They propose a prototype-based emotion transfer framework that can be used in real-world applications.
Outcome:	The proposed framework shows promise but still faces key challenges in the field of emotion recognition in conversation.

Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space (2020.findings-emnlp)

Copied to clipboard

Dongyu Ru, Jiangtao Feng, Lin Qiu, Hao Zhou, Mingxuan Wang, Weinan Zhang, Yong Yu, Lei Li

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

Challenge:	Existing uncertainty sampling methods are time-consuming and can't be executed frequently.
Approach:	They propose adversarial uncertainty sampling in discrete space to find informative unlabeled text samples for annotation using adversarials.
Outcome:	The proposed approach outperforms baselines on effectiveness on five datasets.

Copied to clipboard

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 .

MTG: A Benchmark Suite for Multilingual Text Generation (2022.findings-naacl)

Copied to clipboard

Yiran Chen, Zhenqiao Song, Xianze Wu, Danqing Wang, Jingjing Xu, Jiaze Chen, Hao Zhou, Lei Li

Text Generation

Challenge:	Using MTG, we train and evaluate multilingual text generation models using human-annotated data.
Approach:	They propose a multilingual multiway text generation dataset with 400k human-annotated data that includes four generation tasks across five languages.
Outcome:	The proposed dataset includes four generation tasks across five languages (English, German, French, Spanish and Chinese) it provides comprehensive evaluations with diverse generation scenarios.

Contrastive Aligned Joint Learning for Multilingual Summarization (2021.findings-acl)

Copied to clipboard

Danqing Wang, Jiaze Chen, Hao Zhou, Xipeng Qiu, Lei Li

Text Generation

Challenge:	Existing summarization systems for multilingual text summarizing are limited due to the lack of large-scale data in multiple languages.
Approach:	They propose a multilingual summarization system that can understand documents in multiple languages and generate summaries in the corresponding language.
Outcome:	The proposed model improves over monolingual models in all languages and transferable to other languages.

PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling (2022.coling-1)

Copied to clipboard

Guanting Dong, Daichi Guo, Liwen Wang, Xuefeng Li, Zechen Wang, Chen Zeng, Keqing He, Jinzheng Zhao, Hao Lei, Xinyue Cui, Yi Huang, Junlan Feng, Weiran Xu

WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild? (2025.emnlp-main)

Challenge:	Existing slot filling models memorize inherent patterns of entities and contexts from training data.
Approach:	They propose a perturbed semantic structure awareness transferring method for slot filling models . they use two MLM-based training strategies to learn contextual semantic structure and word distribution .
Outcome:	The proposed method outperforms existing methods and gains strong generalization while preventing model from memorizing inherent patterns of entities and contexts.

Copied to clipboard

An-Lan Wang, Jingqun Tang, Lei Liao, Hao Feng, Qi Liu, Xiang Fei, Jinghui Lu, Han Wang, Hao Liu, Yuliang Liu, Xiang Bai, Can Huang

From Observation to Understanding: Front-Door Adjustments with Uncertainty Calibration for Enhancing Egocentric Reasoning in LVLMs (2025.findings-acl)

Challenge:	Existing benchmarks for document understanding in the wild are based on scanned or digital documents . however, these benchmarks fail to capture the challenges posed by documents in the real world .
Approach:	They propose a new benchmark that incorporates a diverse set of manually captured document images reflecting real-world conditions.
Outcome:	The proposed model is based on a set of manually captured document images reflecting real-world conditions and is compared with digital or scanned documents.

Copied to clipboard

Shenshen Li, Wenxin Meng, Lei Wang, Hao Yang, Chong Peng, Peng Yan, Fumin Shen, Jingkuan Song, Heng Tao Shen, Xing Xu

Low-Resource NLP

Challenge:	Existing methods that adapt LVLMs to egocentric tasks overlook critical agent-environment interactions, limiting their ability to perform egoic reasoning.
Approach:	They propose a zero-shot paradigm to enhance egocentric reasoning by simulating human causal reasoning by formalizing ego-centric reasoning using a structural causal model.
Outcome:	The proposed method improves egocentric reasoning abilities on six tasks.

Can Large Language Models Effectively Support Decision-Making in Sudden Emergencies? (2026.findings-acl)

Copied to clipboard

Mengna Zhu, Jibing Wu, Lihua Liu, Yuran Gong, Yang Hao, Fu Yachao, Mao Wang, Lei Hou, Juanzi Li

ClinAlign: Scaling Healthcare Alignment from Clinician Preference (2026.findings-acl)

Challenge:	Existing research has focused on the earlier stages of emergency response . lack of suitable datasets for reliable and compliance-aware decision-oriented modeling and evaluation is limiting current research .
Approach:	They propose a first real-world emergency decision-making dataset EDM-Bench . they propose 'rule-enhanced reasoning framework' that integrates external regulatory knowledge with constrained inference mechanisms to improve both decision safety and interpretability.
Outcome:	The proposed framework improves decision safety and interpretability by integrating regulatory knowledge with constrained inference mechanisms.

Copied to clipboard

Shiwei Lyu, Xidong Wang, Hao Zhu, Lei Liu, Chaohe Zhang, Jian Wang, Jinjie Gu, Benyou Wang, Yue Shen

Challenge:	Existing methods for aligning open-ended outputs with fine-grained clinician preferences are weakly grounded in professional guidelines.
Approach:	They propose a framework to align large language models' outputs with fine-grained clinician preferences . they propose 119 broadly reusable, clinically grounded principles organized by clinical dimensions .
Outcome:	The proposed framework outperforms existing models on HealthBench-Hard and Deepseek-R1 and o3.

CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models (2024.findings-acl)

Copied to clipboard

Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Noah Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Wenhao Huang, Chenghua Lin, Jie Fu

Do you have the right scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods (2020.acl-main)

Challenge:	a recent study shows that large language models have limited generalization in low-resource languages like Chinese.
Approach:	They propose to evaluate the zero-shot generalizability of large language models to the Chinese language . they release only half of the dataset publicly, with the remainder kept private .
Outcome:	The Chinese Instruction-Following Benchmark evaluates the generalizability of LLMs to the Chinese language.

Copied to clipboard

Ning Miao, Yuxuan Song, Hao Zhou, Lei Li

Rethinking Document-level Neural Machine Translation (2022.findings-acl)

Challenge:	Pre-trained language models can be fine-tuned on task-specific datasets, but fine-timing can lead to over- and/or under-estimation problems.
Approach:	They propose a method to transfer probability mass from over-estimated regions to under-estimates by truncating and transferring probability mass between over- and under-estimating regions.
Outcome:	The proposed method outperforms the fine-tuning approach on a variety of datasets.

Copied to clipboard

Zewei Sun, Mingxuan Wang, Hao Zhou, Chengqi Zhao, Shujian Huang, Jiajun Chen, Lei Li

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

Challenge:	Neural machine translation models are weak enough for document-level translation . current models only translate sentences individually, resulting in poor document coherence .
Approach:	They propose to use the original Transformer model to test document-level neural machine translation . they find that the original transformer models can achieve strong results for document translation if trained properly .
Outcome:	The proposed model outperforms sentence-level models on nine datasets and two sentence- level datasets across six languages.

Copied to clipboard

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.

LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay (2024.emnlp-main)

Copied to clipboard

Yihuai Lan, Zhiqiang Hu, Lei Wang, Yang Wang, Deheng Ye, Peilin Zhao, Ee-Peng Lim, Hui Xiong, Hao Wang

Challenge:	Existing studies on LLM agents' social behaviors are lacking . previous studies focused on positive social behaviors, leaving research on negative social behaviors relatively scarce.
Approach:	They propose a framework that features a multi-agent system facilitating efficient communication and interaction with LLM agents.
Outcome:	The proposed framework is based on Avalon and evaluates on game success and analyzes agents’ social behaviors.

SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)

Copied to clipboard

Daniel Zhang-Li, Joy Jia Yin Lim, Binglin Liu, Shangqing Tu, Zijun Yao, Hao Peng, Jifan Yu, Haoxuan Li, Zhanxin Hao, Ye He, Zekun Li, Jiangyi Wang, Lei Hou, Bin Xu, Xin Cong, Zhiyuan Liu, Huiqin Liu, Yu Zhang, Juanzi Li

Challenge:	Existing LLMs provide partial assistance without modeling these roles, and overly comprehensive help can reduce learner autonomy.
Approach:	They propose a multi-agent framework with an orchestrator agent that provides adaptive scaffolding from interaction logs and collaborator agents that support project work through boundary-aware collaboration.
Outcome:	The proposed framework improves learner examination scores by 14% . it is based on a multi-agent framework with an orchestrator agent .

INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion (2023.findings-emnlp)

Copied to clipboard

Hengchao Shang, Zongyao Li, Daimeng Wei, Jiaxin Guo, Minghan Wang, Xiaoyu Chen, Lizhi Lei, Hao Yang

Text Generation

Challenge:	Existing models for word-level autocompletion (WLAC) only use human typed sequences as prefixes in decoding module.
Approach:	They propose a novel iterative nonautoregressive instruct generation model for WLAC task . it uses human typed sequences and iterating decoding with subwords to fully utilize input information.
Outcome:	The proposed model is more competent in dealing with low-frequency words, and achieves state-of-the-art results on the WMT22 and benchmark datasets.

Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents (2025.emnlp-main)

Copied to clipboard

Haochen Sun, Shuwen Zhang, Lujie Niu, Lei Ren, Hao Xu, Hao Fu, Fangkun Zhao, Caixia Yuan, Xiaojie Wang

Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph (2020.emnlp-main)

Challenge:	Large Language Models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks.
Approach:	They propose a new LLM-based Multi-Agent System benchmark, Collab-Overcooked, built on the popular Overcooked-AI game with more applicable and challenging tasks in interactive environments.
Outcome:	The proposed benchmark provides a multi-agent framework supporting diverse tasks and objectives and encourages collaboration through natural language communication.

Copied to clipboard

Xin Lv, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Wei Zhang, Yichi Zhang, Hao Kong, Suhui Wu

Structured Data in NLP

Challenge:	Existing reasoning methods for sparse KGs are incomplete and lack of evidential paths to target entities makes multi-hop reasoning difficult.
Approach:	They propose a multi-hop reasoning model over sparse KGs to solve this problem . they use latent prediction of embedding-based models to make the model perform more potential path search over sparses .
Outcome:	The proposed method outperforms state-of-the-art models on five datasets from Freebase, NELL and Wikidata.

LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing (2026.findings-acl)

Copied to clipboard

Hao Li, Yiqun Zhang, Zhaoyan Guo, Chenxu Wang, Shengji Tang, Qiaosheng Zhang, Yang Chen, Biqing Qi, Peng Ye, Lei Bai, Zhen Wang, Shuyue Hu

Challenge:	Large language model (LLM) routing assigns each query to the best suitable model from an ensemble.
Approach:	They introduce a large-scale benchmark and unified framework for LLM routing . they find that many routing methods exhibit similar performance under unified evaluation .
Outcome:	The proposed benchmark provides comprehensive metrics for both performance-oriented and performance-cost trade-off routing.

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

Copied to clipboard

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

Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters (2026.acl-long)

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.

Copied to clipboard

Zhiyu Xu, Lean Wang, Yuanxin Liu, Lei Li, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information (2020.emnlp-main)

Challenge:	Existing research lacks systematic analysis of the applicability and methodology of cross-modal skill injection.
Approach:	They investigate the applicability and methodology of cross-modal skill injection by integrating a domain-expert LLM into a VLM.
Outcome:	The proposed method enables transfer of domain-specific expertise from Large Language Models (LLMs) to VLMs without incurring additional training data requirements or significant computational overhead.

Copied to clipboard

Zehui Lin, Xiao Pan, Mingxuan Wang, Xipeng Qiu, Jiangtao Feng, Hao Zhou, Lei Li

Multilinguality

Challenge:	Existing pre-training methods are not effective for machine translation tasks.
Approach:	They propose a method to pre-train a universal multilingual neural machine translation model . they use random aligned substitution technique to bring words and phrases with similar meanings closer in the representation space.
Outcome:	The proposed approach improves translation quality on low, medium, rich resource languages.

R3: End-to-End Reasoning-based Planning for Multi-step Retrosynthesis via Reinforcement Learning (2026.acl-long)

Copied to clipboard

YiFei Wang, Qizhi Pei, Jiangtao Feng, Yuntian Shi, Yi Duan, Lihao Wang, Lei Bai, Lijun Wu, Wei-Ying Ma, Hao Zhou

Reasoning

Challenge:	Experimental results show that R3 is a superior alternative to traditional search algorithms for multistep retrosynthesis planning.
Approach:	They propose a framework that reformulates multistep retrosynthetic planning as a generative reasoning task.
Outcome:	The proposed framework achieves state-of-the-art Top-1 accuracy of 43.7% on retrobench . it leverages Large Language Models to reformulate multistep retrosynthesis as a generative reasoning task.

GeoRA: Geometry-Aware Low-Rank Adaptation for RLVR (2026.acl-long)

Copied to clipboard

Jiaying Zhang, Lei Shi, Jiguo Li, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He

Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management (2026.acl-long)

Challenge:	Existing parameter-efficient methods for RLVR face limitations . low-rank adaptation methods do not account for the distinct optimization dynamics .
Approach:	They propose a low-rank adaptation method tailored for RLVR that exploits the anisotropic structure of RL update subspace and extracts its principal directions via Singular Value Decomposition (SVD).
Outcome:	Experiments on large reasoning models show that GeoRA outperforms strong low-rank baselines across RLVR settings while showing stronger generalization and less forgetting on out-of-domain tasks.

Copied to clipboard

Weitao Ma, Xiaocheng Feng, Lei Huang, Xiachong Feng, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Bing Qin

UCL-Bench: A Chinese User-Centric Legal Benchmark for Large Language Models (2025.findings-naacl)

Challenge:	Existing approaches to memory management rely on final task performance as the primary reward, resulting in severe reward sparsity and ineffective credit assignment.
Approach:	They propose a framework for fine-grained feedback alignment using a Chunk-level step reward and Evidence-Anchored Reward Attribution to redistribute global rewards based on memory items utilized as evidence in reasoning.
Outcome:	The proposed framework outperforms baselines and supports generalization across different model configurations and backbones.

Copied to clipboard

Ruoli Gan, Duanyu Feng, Chen Zhang, Zhihang Lin, Haochen Jia, Hao Wang, Zhenyang Cai, Lei Cui, Qianqian Xie, Jimin Huang, Benyou Wang

Natural Language Interfaces

Challenge:	Existing legal benchmarks focusing on knowledge and logic evaluate LLMs on various tasks in legal domain, but few have explored the practical application of LLM by actual users.
Approach:	They propose a Chinese user-centric legal benchmark that aims to assess the practical application of LLMs by real users.
Outcome:	The proposed model outperforms existing models on various tasks in legal domain but does not outperfect ChatGPT.

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

Copied to clipboard

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

Learning Logic Rules for Document-Level Relation Extraction (2021.emnlp-main)

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.

Copied to clipboard

Dongyu Ru, Changzhi Sun, Jiangtao Feng, Lin Qiu, Hao Zhou, Weinan Zhang, Yong Yu, Lei Li

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

Challenge:	Existing models for document-level relation extraction relied on implicitly powerful representations, which makes the model less transparent.
Approach:	They propose a probabilistic model for document-level relation extraction by learning logic rules.
Outcome:	The proposed model outperforms baseline models in relation performance and logical consistency.

Copied to clipboard

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

LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts (2025.acl-long)

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.

Copied to clipboard

Qibing Ren, Hao Li, Dongrui Liu, Zhanxu Xie, Xiaoya Lu, Yu Qiao, Lei Sha, Junchi Yan, Lizhuang Ma, Jing Shao

Dynamically Fused Graph Network for Multi-hop Reasoning (P19-1)

Challenge:	Current safety training focuses on teaching models to reject harmful queries, but recent research shows that adversarial attacks or jailbreak methods bypass these safety mechanisms.
Approach:	They propose to use a new attack method to craft multi-turn toxic prompts that gradually lead LLMs to reveal unsafe content.
Outcome:	The proposed method outperforms existing methods in diversity, effectiveness, and efficiency across aligned LLMs.

Copied to clipboard

Lin Qiu, Yunxuan Xiao, Yanru Qu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu

Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment (2025.emnlp-main)

Challenge:	Text-based question answering (TBQA) has been studied extensively in recent years.
Approach:	They propose a Dynamically Fused Graph Network to answer questions requiring multiple scattered evidence and reasoning over them.
Outcome:	The proposed method achieves competitive results on a public TBQA dataset and produces interpretable reasoning chains.

Copied to clipboard

Hao Li, Lijun Li, Zhenghao Lu, Xianyi Wei, Rui Li, Jing Shao, Lei Sha

Contextual Representation Learning beyond Masked Language Modeling (2022.acl-long)

Challenge:	Recent studies show that fine-tuning with benign data can compromise safety of aligned LLMs.
Approach:	They propose a Layer-Aware Representation Filtering method that detects safety-degrading layers within the LLM and leverages their representations to detect them.
Outcome:	The proposed method can detect safety-degrading features in benign data and remove them from the model.

Copied to clipboard

Zhiyi Fu, Wangchunshu Zhou, Jingjing Xu, Hao Zhou, Lei Li

METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models (2026.acl-long)

Challenge:	masked language models adopt sampled embeddings as anchors to estimate and inject contextual semantics to representations.
Approach:	They propose a representation learning approach that uses embeddings as anchors to model contextual representations.
Outcome:	The proposed model achieves 5x speedup and 1.2 points average improvement over MLM.

Copied to clipboard

Pengfeng Li, Chen Huang, Chaoqun Hao, Hongyao Chen, Xiao-Yong Wei, Wenqiang Lei, See-Kiong Ng

Pre-training Distillation for Large Language Models: A Design Space Exploration (2025.acl-long)

Challenge:	Existing benchmarks evaluate contextual causal reasoning in fragmented settings, failing to ensure context consistency or cover the full causal hierarchy.
Approach:	They use a unified context to benchmark large language models' contextual causal reasoning skills.
Outcome:	The proposed benchmarks show that LLMs are susceptible to distraction by irrelevant but factually correct information at lower level of causality.

Copied to clipboard

Hao Peng, Xin Lv, Yushi Bai, Zijun Yao, Jiajie Zhang, Lei Hou, Juanzi Li

Probabilistic Graph Reasoning for Natural Proof Generation (2021.findings-acl)

Challenge:	Knowledge distillation (KD) aims to transfer knowledge from a large teacher model to a smaller student model for model compression.
Approach:	They extend knowledge distillation to the pre-training phase of large language models . they first conduct an experiment using a teacher LLM to distill a 1.9B student LLM .
Outcome:	The proposed model can be used to distill a 1.9B student model using a teacher LLM.

Copied to clipboard

Changzhi Sun, Xinbo Zhang, Jiangjie Chen, Chun Gan, Yuanbin Wu, Jiaze Chen, Hao Zhou, Lei Li

Structured Data in NLP

Challenge:	Existing approaches to reasoning over formal representations do not explicitly consider inter-dependency between answers and proofs.
Approach:	They propose a novel approach for joint answer prediction and proof generation using an induced graphical model.
Outcome:	The proposed approach achieves 10%-30% improvement on QA accuracy in evaluations under diverse conditions.

Vocabulary Learning via Optimal Transport for Neural Machine Translation (2021.acl-long)

Copied to clipboard

Jingjing Xu, Hao Zhou, Chun Gan, Zaixiang Zheng, Lei Li

Machine Translation

Challenge:	Empirical results show that VOLT beats widely-used vocabularies in diverse scenarios, including WMT-14 English-German translation, TED bilingual translation, and TED multilingual translation.
Approach:	They propose a token dictionary solution that can be used without trial training to find the best dictionary with a proper size.
Outcome:	The proposed solution beats widely-used vocabularies in English-German translation, TED bilingual translation, and TED multilingual translation.

DiffusionAttacker: Diffusion-Driven Prompt Manipulation for LLM Jailbreak (2025.emnlp-main)

Copied to clipboard

Hao Wang, Hao Li, Junda Zhu, Xinyuan Wang, Chengwei Pan, Minlie Huang, Lei Sha

QaRL: Rollout-Aligned Quantization-Aware RL for Fast and Stable Training under Training–Inference Mismatch (2026.findings-acl)

Challenge:	Large Language Models (LLMs) are susceptible to generating harmful content when prompted with carefully crafted inputs, a vulnerability known as LLM jailbreaking.
Approach:	They propose an end-to-end generative approach for jailbreak rewriting inspired by diffusion models that uses a sequence-tosequence (seq2sequ) diffusion model as a generator, conditioning on the original prompt and guiding the denoising process with a novel attack loss.
Outcome:	Experiments on Advbench and Harmbench show that the proposed method outperforms autoregressive jailbreak models across evaluation metrics including ASR, fluency, diversity and diversity.

Copied to clipboard

Hao Gu, Hao Wang, Jiacheng Liu, Lujun Li, Qiyuan Zhu, Bei Liu, Binxing Xu, Lei Wang, Xintong Yang, Sida Lin, Sirui Han, Yike Guo

Challenge:	Recent work has shown that reinforcement learning with simple rule-based reward functions (RLVR) can induce emergent reasoning behaviors and yield gains in challenging domains such as math problem solving.
Approach:	They propose a rollout-alignment-quantization-aware RL which aligns training-side forward with the quantized rollout to minimize mismatch.
Outcome:	The proposed approach outperforms quantized-rollout training by +5.5 on Qwen3-30B-A3B MoE for math problems while maintaining low-bit throughput.

Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning (2023.emnlp-main)

Copied to clipboard

Lean Wang, Lei Li, Damai Dai, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

Glancing Transformer for Non-Autoregressive Neural Machine Translation (2021.acl-long)

Challenge:	In-context learning (ICL) is a promising capability for large language models (LLMs) but its underlying mechanism remains unexplored.
Approach:	They propose a demonstration compression technique to expedite inference and an analysis framework for diagnosing ICL errors in GPT2-XL.
Outcome:	The proposed method improves ICL performance and expedites inference.

Copied to clipboard

Lihua Qian, Hao Zhou, Yu Bao, Mingxuan Wang, Lin Qiu, Weinan Zhang, Yong Yu, Lei Li

Simulating Classroom Education with LLM-Empowered Agents (2025.naacl-long)

Challenge:	Existing non-autoregressive neural machine translation methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup.
Approach:	They propose a Glancing Language Model (GLM) for single-pass parallel generation models and Glancing Transformer (GLAT) with only single- pass decoding, GLAT is able to generate high-quality translation with 8-15 speedup.
Outcome:	The proposed model outperforms all previous non-autoregressive methods on multiple language directions and is nearly comparable to Transformer.

Copied to clipboard

Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianxiao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, Lei Hou, Juanzi Li

latent-GLAT: Glancing at Latent Variables for Parallel Text Generation (2022.acl-long)

Challenge:	Initial studies have focused on task-specific, independent LLM-empowered agents, but the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored.
Approach:	They propose a multi-agent classroom simulation teaching framework that recognizes representative class roles and introduces a novel class control mechanism for automatic classroom teaching.
Outcome:	The proposed framework can simulate dynamic learning environment for users with active teacher-student and student-studente interactions.

Copied to clipboard

Yu Bao, Hao Zhou, Shujian Huang, Dongqi Wang, Lihua Qian, Xinyu Dai, Jiajun Chen, Lei Li

SafetyMem: Adaptive Jailbreak Defense via Dual-Component Safety Memory (2026.acl-long)

Challenge:	Recent advances in text generation have limited applications due to multimodality problem.
Approach:	They propose a method which uses latent variables to capture word categorical information and invoke an advanced curriculum learning technique to overcome multi-modality problem.
Outcome:	The proposed method outperforms strong baselines without an autoregressive model, which further broadens the application scenarios of the parallel decoding paradigm.

Copied to clipboard

Hao Wang, Ziyi Ni, Huacan Wang, Pin Lyu, Lei Sha

ASETF: A Novel Method for Jailbreak Attack on LLMs through Translate Suffix Embeddings (2024.emnlp-main)

Challenge:	Existing defenses for Large Language Models suffer from a 'memory gap' parameter-modifying methods are computationally expensive and inference-time filters cannot retain or reuse defense knowledge across interactions.
Approach:	They propose a framework that secures Large Language Models through a dual-component safety memory system.
Outcome:	The proposed framework significantly reduces attack success rates while preserving interpretability and efficiency.

Copied to clipboard

Hao Wang, Hao Li, Minlie Huang, Lei Sha

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

Challenge:	Attaching suffixes to harmful instructions can hack the defense of Large language models (LLMs) However, due to the unreadable of adversarial suffix, it can be relatively easily penetrated by common defense methods such as perplexity filters.
Approach:	They propose an algorithm to embed adversarial suffixes into coherent and understandable text to attack Large language models (LLMs) using a Advbench dataset.
Outcome:	The proposed approach reduces the computation time of adversarial suffixes and achieves a much better attack success rate than existing techniques.

Copied to clipboard

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

Evaluation Dataset for Lexical Translation Consistency in Chinese-to-English Document-level Translation (2024.lrec-main)

Challenge:	Event factuality detection is under-explored due to the lack of high-quality large-scale data . efd is a subfield of event understanding, which aims to determine the factuity of textual events.
Approach:	They propose a large-scale EFD dataset with factuality annotations of 112,276 events . they find that adopting event arguments and relations helps in event factuity detection .
Outcome:	The proposed dataset includes factuality annotations of 112,276 events . it is the largest EFD dataset and is challenging for fine-tuned models and large language models .

Copied to clipboard

Xiangyu Lei, Junhui Li, Shimin Tao, Hao Yang

Multilinguality

Challenge:	Existing studies on document-level neural machine translation (NMT) assume that all repeated source words should be translated consistently.
Approach:	They construct a test set of 310 bilingual news articles to evaluate lexical translation consistency.
Outcome:	The proposed test sets show that translation consistency is consistent across multiple languages.

Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent (2024.emnlp-main)

Copied to clipboard

Xiaoyan Yu, Tongxu Luo, Yifan Wei, Fangyu Lei, Yiming Huang, Hao Peng, Liehuang Zhu

Green & Sustainable NLP

Challenge:	Large Language Models (LLMs) have revolutionized open-domain dialogue agents but face challenges in multi-character role-playing (MCRP) scenarios.
Approach:	They propose a framework for efficient multi-character role-playing that employs a dynamic low-rank adapter strategy and distinct LoRA blocks for each character.
Outcome:	Neeko employs a dynamic low-rank adapter (LoRA) strategy, enabling it to adapt seamlessly to diverse characters.

MTRouter: Cost-Aware Multi-Turn LLM Routing with History–Model Joint Embeddings (2026.acl-long)

Copied to clipboard

Yiqun Zhang, Hao Li, Zihan Wang, Shi Feng, Xiaocui Yang, Daling Wang, Bo Zhang, Lei Bai, Shuyue Hu

Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems (2025.acl-long)

Challenge:	Multi-turn, long-horizon tasks require dozens of sequential model calls per episode.
Approach:	They propose a cost-aware multi-turn LLM routing tool which encodes interaction history and candidate models into joint history–model embeddings and learns an outcome estimator from logged trajectories to predict turn-level model utility.
Outcome:	The proposed model reduces cost and performance by 58.7% on ScienceWorld and on Humanity’s Last Exam (HLE) and even reduces costs for held-out tasks.

Copied to clipboard

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

Challenge:	Existing reward models focus on human preferences, neglecting verifiable correctness signals.
Approach:	They propose a reward system that combines human preference rewards with verifiable correctness signals to provide reliable rewards.
Outcome:	The proposed reward agent significantly outperforms vanilla reward models on benchmarks and inference-time best-of-n searches on real-world tasks.

What Factors Influence LLMs’ Judgments? A Case Study on Question Answering (2024.lrec-main)

Copied to clipboard

Lei Chen, Bobo Li, Li Zheng, Haining Wang, Zixiang Meng, Runfeng Shi, Hao Fei, Jun Zhou, Fei Li, Chong Teng, Donghong Ji

Natural Language Interfaces

Challenge:	Existing studies indicate that Large Language Models perform at a level comparable to humans with advantages of speed and cost-effectiveness in different fields.
Approach:	They propose to introduce four unexplored factors and a new dimension of question difficulty to provide a more comprehensive understanding of LLMs’ judgments across varying question intricacies.
Outcome:	The proposed dimensions of question difficulty and answer quantity provide valuable insights into optimizing LLMs’ performance as judges.

MMD-ERE: Multi-Agent Multi-Sided Debate for Event Relation Extraction (2025.coling-main)

Copied to clipboard

Yong Guan, Hao Peng, Lei Hou, Juanzi Li

Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting (2023.findings-acl)

Challenge:	Existing research indicates that LLMs can be overconfident and stubborn.
Approach:	They propose a multi-agent multi-sided debate approach for event relation extraction which explores the understanding of event relations between different participants before and after the debate.
Outcome:	The proposed approach outperforms established baselines on various ERE tasks and LLMs.

Copied to clipboard

Xuefeng Li, Liwen Wang, Guanting Dong, Keqing He, Jinzheng Zhao, Hao Lei, Jiachi Liu, Weiran Xu

Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting (2025.findings-acl)

Challenge:	Existing approaches to slot filling only learn surface mapping of slot types between D S and D T and get poor generalization capability or robustness.
Approach:	They propose a generative zero-shot prompt learning framework for cross-domain slot filling which improves generalization and robustness than previous work.
Outcome:	The proposed framework improves generalization and robustness on unseen slots and an efficient prompt tuning strategy boosts performance.

Copied to clipboard

Hao Feng, Shu Wei, Xiang Fei, Wei Shi, Yingdong Han, Lei Liao, Jinghui Lu, Binghong Wu, Qi Liu, Chunhui Lin, Jingqun Tang, Hao Liu, Can Huang

Speak No Evil, Just Prompt: Low-resource Multilingual Toxic Speech Detection with Audio Language Model (2026.findings-acl)

Challenge:	Current document image parsing solutions rely on specialized models or generate content autoregressively.
Approach:	They propose a multimodal document image parsing model that integrates specialized models with autogeneous content generation.
Outcome:	The proposed model achieves state-of-the-art performance across diverse page-level and element-level settings while ensuring superior efficiency.

Copied to clipboard

Mingzi Zuo, Lei Zhang, Hailiang Sun, Shengzhi Huo, Changyu Dong, Xin Wang, Bo Wang, Hao Liu

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (2022.acl-long)

Challenge:	Existing methods for toxic speech detection rely on high-resource languages and lack acoustic cues.
Approach:	They propose a prompt-based adaptation framework that performs end-to-end toxicity detection without ASR.
Outcome:	The proposed framework achieves a micro-averaged ROC-AUC of 98.07% on polySpeechTox . it is based on a frozen audio language model and can perform end-to-end toxicity detection without ASR .

Copied to clipboard

Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen

How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and Activation (2026.findings-acl)

Challenge:	Existing methods to train dense passage retrieval have a large data gap between upstream and downstream relevance.
Approach:	They propose a method to pre-train the dense retriever with the text relevance induced by hyperlinks within Web documents.
Outcome:	The proposed method outperforms existing methods under different scenarios and in the open-domain question answering domain.

Copied to clipboard

Hao Yang, Qinghua Zhao, Lei Li, Lingyi Meng, Mengda Yu

ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)

Challenge:	Chain-of-Thought (CoT) prompting significantly enhances model reasoning, yet its internal mechanisms remain poorly understood.
Approach:	They reversely traced information flow across decoding, projection, and activation phases and found that CoT may serve as a decoding space pruner .
Outcome:	The proposed framework can be used to design more efficient and robust prompts.

Copied to clipboard

Yijun Wang, Changzhi Sun, Yuanbin Wu, Hao Zhou, Lei Li, Junchi Yan