Papers by Mao Li

VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models (2024.emnlp-main)

Challenge:	Existing approaches to improve long-chain mathematical reasoning focus on the first erroneous step, but ignore all other steps and rely heavily on external signals.
Approach:	They propose a DPO framework that leverages step-wise rewards from the entire reasoning chain instead of optimizing only the first erroneous step.
Outcome:	The proposed framework improves on in-domain and out-of-domain mathematical reasoning benchmarks.

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Yifei Liu, Jicheng Wen, Yang Wang, Shengyu Ye, Li Lyna Zhang, Ting Cao, Cheng Li, Mao Yang

Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation (2022.naacl-main)

Challenge:	Recent research has focused on pushing weight-only quantization to extremely low-bit due to numerical representation limitations.
Approach:	They propose a vector-based quantization approach that pushes LLMs to extremely low-bit . they propose scalar-based weight quantization that reduces memory requirements and optimizes storage costs .
Outcome:	The proposed method reduces model quantization perplexity by 0.01-0.34 on LLaMA-2, 0.38-0.68 on mistral-7B, 4.41-7.34, on llaMA-3 on QA tasks on average.

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Yu Li, Baolin Peng, Yelong Shen, Yi Mao, Lars Liden, Zhou Yu, Jianfeng Gao

XLM-E: Cross-lingual Language Model Pre-training via ELECTRA (2022.acl-long)

Challenge:	Existing knowledge-grounded dialogue systems perform poorly on unseen topics due to limited topics covered in training data.
Approach:	They propose a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks.
Outcome:	The proposed language model generalizes well across knowledge-grounded dialogue tasks.

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Zewen Chi, Shaohan Huang, Li Dong, Shuming Ma, Bo Zheng, Saksham Singhal, Payal Bajaj, Xia Song, Xian-Ling Mao, Heyan Huang, Furu Wei

LAFaCT: Attribution-based Localization and Focused Sequential Analysis of Fact-Critical Tokens for Hallucination Detection (2026.acl-long)

Challenge:	ELECTRA-style tasks are used to pretrain cross-lingual models for NLP tasks . masked language modeling tasks require massive computation resources, rendering such models quite expensive .
Approach:	They propose to use ELECTRA-style tasks to pre-train a cross-lingual language model . they propose to pretrain the model on multilingual and parallel corpora .
Outcome:	The proposed model outperforms baseline models on cross-lingual understanding tasks with much less computation cost.

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Xin Wang, Jiahao Li, Licheng Zhang, Zhendong Mao

From Charts to Code: A Hierarchical Benchmark for Multimodal Models (2026.acl-long)

Challenge:	Large Language Models suffer from hallucinations, severely undermining their reliability.
Approach:	They propose a framework that localizes fact-critical tokens and performs sequential analysis on their hidden states.
Outcome:	The proposed framework localizes fact-critical tokens using Factual Criticality . it then performs a focused sequential analysis on their hidden states .

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Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, Zijian Zhang, Yifei Tao, Dongxing Mao, Yang Wan, Jingru Tan, Min Zeng, Min Li, Alex Jinpeng Wang

Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models (2025.findings-acl)

Challenge:	Chart2Code is a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models.
Approach:	They introduce Chart2Code, a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models.
Outcome:	The proposed benchmark is the first to scale task complexity while capturing diverse scenarios.

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Xi Li, Ruofan Mao, Yusen Zhang, Renze Lou, Chen Wu, Jiaqi Wang

Robustness in NLP

Challenge:	Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, but are vulnerable to backdoor attacks.
Approach:	They propose a chain-of-scrutiny approach which leverages LLMs’ unique reasoning abilities to mitigate backdoor attacks.
Outcome:	The proposed model is well-suited for the popular API-only LLM deployments, enabling detection at minimal cost and with little data.

Feature-Adaptive and Data-Scalable In-Context Learning (2024.acl-long)

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Jiahao Li, Quan Wang, Licheng Zhang, Guoqing Jin, Zhendong Mao

Youling: an AI-assisted Lyrics Creation System (2020.emnlp-demos)

Challenge:	In-context learning (ICL) is a popular way to stimulate LLM capabilities for downstream tasks due to context length constraints.
Approach:	They propose a feature-adaptive and data-scalable in-context learning framework which leverages task-adaptives to promote inference on the downstream task.
Outcome:	The proposed framework outperforms state-of-the-art methods on 10 datasets under different data settings and LLM scale.

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Rongsheng Zhang, Xiaoxi Mao, Le Li, Lin Jiang, Lin Chen, Zhiwei Hu, Yadong Xi, Changjie Fan, Minlie Huang

Challenge:	Recent studies have focused on a single pass of lyrics generation with little human intervention.
Approach:	They propose an AI-assisted lyrics creation system that supports one pass full-text generation and interactive generation modes.
Outcome:	The proposed system supports full-text generation and interactive generation modes . it also provides a revision module which enables users to revise undesired lyrics repeatedly.

ALERT: An LLM-powered Benchmark for Automatic Evaluation of Recommendation Explanations (2025.naacl-long)

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Yichuan Li, Xinyang Zhang, Chenwei Zhang, Mao Li, Tianyi Liu, Pei Chen, Yifan Gao, Kyumin Lee, Kaize Ding, Zhengyang Wang, Zhihan Zhang, Jingbo Shang, Xian Li, Trishul Chilimbi

Can Monolingual Pretrained Models Help Cross-Lingual Classification? (2020.aacl-main)

Challenge:	Existing benchmarks for recommendation explanation evaluation lack item diversity and user preferences data.
Approach:	They propose a model-agnostic recommendation explanation evaluation benchmark based on Amazon e-commerce categories with implicit preferences . they propose two novel automatic evaluators that enable scalable and human-preference aligned evaluation of explanations .
Outcome:	The proposed model-agnostic evaluation benchmark outperforms existing methods in a variety of domains.

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Zewen Chi, Li Dong, Furu Wei, Xianling Mao, Heyan Huang

Multilinguality

Challenge:	Multilingual pretrained language models have shown impressive results for cross-lingual transfer, but due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors.
Approach:	They propose to transfer the knowledge from monolingual pretrained models to multilingual ones to improve zero-shot cross-lingual classification by using machine translation systems.
Outcome:	The proposed methods outperform vanilla multilingual fine-tuning on two cross-lingual classification benchmarks.

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

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

Rethinking Prompt Optimizers: From Prompt Merits to Optimization (2026.eacl-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.

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Zixiao Zhu, Hanzhang Zhou, Zijian Feng, Tianjiao Li, Chua Jia Jim Deryl, Lee Onn Mak, Gee Wah Ng, Kezhi Mao

Zero-Shot Detection of LLM-Generated Text using Temperature Sensitivity (2026.acl-long)

Challenge:	Existing methods to optimize prompts rely on LLMs' self-generation ability but lack interpretability due to implicit optimization.
Approach:	They propose a model-agnostic prompt quality merits and a merit-guided, locally deployable prompt optimizer trained on a lightweight LLM to improve prompt quality.
Outcome:	The proposed model avoids online optimization, reduces privacy concerns, and generalizes effectively to both large-scale and lightweight inference models.

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Shixuan Ma, Jiahao Li, Zhendong Mao, Quan Wang

mT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs (2021.emnlp-main)

Challenge:	Existing methods for detecting LLM-generated text rely on statistical features that are insufficient for reliable detection.
Approach:	They propose a temperature-sensitive detector that modulates decoding temperature and monitors how probability distributions respond to temperature.
Outcome:	The proposed method is based on a temperature sensitivity feature and a simple zero-shot detector built upon normalized temperature sensitivity.

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Zewen Chi, Li Dong, Shuming Ma, Shaohan Huang, Saksham Singhal, Xian-Ling Mao, Heyan Huang, Xia Song, Furu Wei

Multilinguality

Challenge:	Multilingual T5 pretrains a sequence-to-sequence model on monolingual texts, but it has shown promising results on many cross-lingual tasks.
Approach:	They propose a partially non-autoregressive objective for text-to-text pre-training and propose mT6 to improve cross-lingual transferability over multilingual T5.
Outcome:	The proposed model improves cross-lingual transferability over existing models.

Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion (2022.findings-acl)

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Yiqing Xie, Jiaming Shen, Sha Li, Yuning Mao, Jiawei Han

FastCuRL: Curriculum Reinforcement Learning with Stage-wise Context Scaling for Efficient Training R1-like Reasoning Models (2025.findings-emnlp)

Challenge:	Document-level relation extraction (DocRE) aims to extract semantic relations among entity pairs in a document.
Approach:	They propose an evidence-enhanced framework that empowers document-level relation extraction (DocRE) Eider efficiently extracts evidence and effectively fuses extracted evidence in inference.
Outcome:	The proposed framework outperforms state-of-the-art methods on three benchmark datasets.

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Mingyang Song, Mao Zheng, Zheng Li, Wenjie Yang, Xuan Luo

Gated Mechanism Enhanced Multi-Task Learning for Dialog Routing (2022.coling-1)

Challenge:	Improving training efficiency remains a challenge in large-scale Reinforcement Learning (RL).
Approach:	They propose a curriculum RL framework with stage-wise context scaling to improve RL training efficiency.
Outcome:	The proposed framework outperforms state-of-the-art reasoning models on five benchmarks and achieves 49.6% accuracy on AIME 2024.

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Ziming Huang, Zhuoxuan Jiang, Ke Wang, Juntao Li, Shanshan Feng, Xian-Ling Mao

Low-Resource NLP

Challenge:	Existing methods for dialog routing are mostly heuristic and cannot achieve high-quality performance.
Approach:	They propose a multi-task learning framework with a dialog encoder and two tailored gated mechanism modules to solve this problem.
Outcome:	The proposed model can play the role of hierarchical information filtering and is non-invasive to existing dialog systems.

Automated Creativity Evaluation for Large Language Models: A Reference-Based Approach (2025.findings-emnlp)

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Ruizhe Li, Chiwei Zhu, Benfeng Xu, Xiaorui Wang, Zhendong Mao

UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations (2025.acl-long)

Challenge:	Existing methods for evaluating creativity of machine-generated texts rely on costly manual annotations or fail to align closely with human assessments.
Approach:	They propose an automated method based on the Torrance Test of Creative Writing (TTCW) .
Outcome:	The proposed method improves the alignment between LLM evaluations and human assessments.

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Fengran Mo, Yifan Gao, Chuan Meng, Xin Liu, Zhuofeng Wu, Kelong Mao, Zhengyang Wang, Pei Chen, Zheng Li, Xian Li, Bing Yin, Meng Jiang

LATENTLOGIC: Learning Logic Rules in Latent Space over Knowledge Graphs (2023.findings-emnlp)

Challenge:	Existing conversational search systems are usually built with two different models . this separation restricts the system from leveraging the model's intrinsic knowledge simultaneously . Existing studies for developing unified models cannot fully address the aspects of understanding conversational context, managing retrieval independently, and generating responses.
Approach:	They propose to unify dense retrieval and response generation for large language models in conversation by fine-tuning and mitigating data discrepancy.
Outcome:	The proposed model can outperform existing models on five conversational search datasets and reduce inconsistency risks while mitigating data discrepancy.

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Junnan Liu, Qianren Mao, Chenghua Lin, Yangqiu Song, Jianxin Li

Green & Sustainable NLP

Challenge:	Existing methods for learning logic rules for knowledge graph reasoning face limitations such as searching in vast search space and inefficient optimization.
Approach:	They propose a framework to efficiently mine logic rules by controllable generation in the latent space by a pre-trained VAE and a discriminator.
Outcome:	The proposed framework efficiently mines logic rules by controllable generation in the latent space.

Dynamic Online Conversation Recommendation (2020.acl-main)

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Xingshan Zeng, Jing Li, Lu Wang, Zhiming Mao, Kam-Fai Wong

Towards Explainable Chinese Native Learner Essay Fluency Assessment: Dataset, Tasks, and Method (2024.findings-emnlp)

Challenge:	Existing models that assume static user interests are unable to capture the temporal aspects of user interactions and interest changes over time.
Approach:	They propose a neural architecture to exploit changes of user interactions and interests over time to predict which discussions they are likely to enter.
Outcome:	The proposed model outperforms state-of-the-art models that assume static user interests and handle future conversations that are unseen during training time.

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Xinshu Shen, Hongyi Wu, Yadong Zhang, Man Lan, Xiaopeng Bai, Shaoguang Mao, Yuanbin Wu, Xinlin Zhuang, Li Cai

LaTeX2Solver: a Hierarchical Semantic Parsing of LaTeX Document into Code for an Assistive Optimization Modeling Application (2023.acl-demo)

Challenge:	Existing GEC datasets in Chinese fail to consider specific grammatical error types and overlook cross-sentence grammamatical errors.
Approach:	They propose to use Chinese essay fluency assessment to assess essay fluencies along with coarse and fine-grained errors and corrections to improve explainability.
Outcome:	The proposed dataset encapsulates essay fluency scores along with both coarse and fine-grained errors and corrections.

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Rindra Ramamonjison, Timothy Yu, Linzi Xing, Mahdi Mostajabdaveh, Xiaorui Li, Xiaojin Fu, Xiongwei Han, Yuanzhe Chen, Ren Li, Kun Mao, Yong Zhang

Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking (2021.findings-acl)

Challenge:	Existing systems that translate optimization formulas manually are cumbersome and time-consuming.
Approach:	They propose a system that converts optimization formulas from TeX document to solver language.
Outcome:	The proposed system helps operations research practitioners convert optimization formulations into solver modeling languages.

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Heng-Da Xu, Zhongli Li, Qingyu Zhou, Chao Li, Zizhen Wang, Yunbo Cao, Heyan Huang, Xian-Ling Mao

EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering (2026.acl-long)

Challenge:	Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for usergenerated text in Chinese.
Approach:	They propose a Chinese spell checker that leverages multimodal Chinese characters' information to predict the correct output.
Outcome:	The proposed model outperforms strong baselines on the SIGHAN benchmarks by a large margin.

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Chenhui Mao, Yuanting Lei, Zhixiang Wei, Ming Liang, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li

Green & Sustainable NLP

Challenge:	Entropy-Guided Stepwise Scaling (EGSS) is a novel TTS framework for software engineering tasks.
Approach:	They propose an entropy-guided stepwise scaling framework that balances efficiency and effectiveness through entropic-guide encoding and robust test-suite augmentation.
Outcome:	EGSS boosts performance by 5–10% across all evaluated models, and reduces inference-time token usage by over 28% . compared to existing methods, EGS reduces token usage and reduce inference time by over 20% .

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)

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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation (2025.findings-acl)

Challenge:	Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures.
Approach:	They propose a toolkit that supports pre-training models of different modalities.
Outcome:	The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks.

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Huimin Xu, Xin Mao, Feng-Lin Li, Xiaobao Wu, Wang Chen, Wei Zhang, Anh Tuan Luu

DIGAT: Modeling News Recommendation with Dual-Graph Interaction (2022.findings-emnlp)

Challenge:	Existing process annotation approaches are computationally expensive.
Approach:	They propose a compression-based approach that transforms reasoning steps into code and normalizes them through Abstract Syntax Tree.
Outcome:	The proposed method outperforms existing methods on Best-of-N strategy and ProcessBench.

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Zhiming Mao, Jian Li, Hongru Wang, Xingshan Zeng, Kam-Fai Wong

Structured Data in NLP

Challenge:	Existing news recommendation methods lack effective news-user feature interaction.
Approach:	They propose to use news-graph and user-graph channels to enhance news encodings . they also propose to perform effective feature interaction between news and user graphs based on semantic-augmented graphs.
Outcome:	The proposed graph attention networks outperform existing NR methods on the benchmark dataset MIND.

How do LLMs’ Preferences Affect Event Argument Extraction? CAT: Addressing Preference Traps in Unsupervised EAE (2025.findings-acl)

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Yunhao Wei, Kai Shuang, Zhiyi Li, Chenrui Mao

A Joint Learning Framework for Restaurant Survival Prediction and Explanation (2022.emnlp-main)

Challenge:	Existing approaches to supervised EAE suffer from preference traps due to misalignments between prior knowledge, instructions, or output constraints and LLMs’ preferences.
Approach:	They propose an unsupervised EAE framework that handles LLMs' preference traps by targeting their prior knowledge and instructions.
Outcome:	The proposed framework matches the best DeepSeek-R1 API model with a significantly lower time cost.

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Xin Li, Xiaojie Zhang, Peng JiaHao, Rui Mao, Mingyang Zhou, Xing Xie, Hao Liao

Explainability & Interpretability in NLP

Challenge:	Recent advances in deep learning have various models that research reviews and interactions for different kinds of tasks, such as predicting restaurant survival.
Approach:	They propose a joint learning framework for explainable restaurant survival prediction based on multi-modal data of user-restaurant interactions and users’ textual reviews.
Outcome:	The proposed framework improves on two datasets showing that it can model restaurant interactions and users’ textual reviews.

Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders (2023.findings-emnlp)

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Qianren Mao, Shaobo Zhao, Jiarui Li, Xiaolei Gu, Shizhu He, Bo Li, Jianxin Li

Don’t Forget Your Reward Values: Language Model Alignment via Value-based Calibration (2024.emnlp-main)

Challenge:	Existing methods to rank sentences using pre-trained embeddings create a gap due to different optimization objectives.
Approach:	They propose a pre-trained embedding process that optimizes informative sentences . they use sentence-word bipartite graphs to model intra-sentential distinctive features .
Outcome:	The proposed model outperforms heavy BERT- or RoBERTa-based sentence ranking methods by providing summary-worthy representations.

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Xin Mao, Feng-Lin Li, Huimin Xu, Wei Zhang, Wang Chen, Anh Tuan Luu

Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization (2022.coling-1)

Challenge:	Existing methods for generating large language models have been criticized for their complexity and instability.
Approach:	They propose a value-based calibration method to better align Large Language Models with human preferences.
Outcome:	The proposed method surpasses existing methods on AI assistant and summarization datasets, providing impressive generalizability, robustness, and diversity in different settings.

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Yiming Wang, Qianren Mao, Junnan Liu, Weifeng Jiang, Hongdong Zhu, Jianxin Li

COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition (2022.coling-1)

Challenge:	Existing studies on semi-supervised learning methods focus on how to effectively utilize abundant unlabeled data.
Approach:	They propose a semi-supervised consistency training method to regularize model predictions and a pseudo-labeling strategy to obtain high-confidence labels from unlabeled predictions.
Outcome:	The proposed method improves extractive summarization over an insufficient labeled dataset.

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Yucheng Huang, Kai He, Yige Wang, Xianli Zhang, Tieliang Gong, Rui Mao, Chen Li

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

Challenge:	Existing methods for Named Entity Recognition (NER) use a similarity metric to measure semantic similarity between test samples and referents, but their performance is limited due to the label scarcity.
Approach:	They propose a novel approach to learn a similarity metric for measuring the semantic similarity between test samples and referents, where each referent represents an entity class.
Outcome:	The proposed approach outperforms state-of-the-art models with a significant margin in most cases.

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

Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction (2022.emnlp-main)

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.

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Zhen Wan, Qianying Liu, Zhuoyuan Mao, Fei Cheng, Sadao Kurohashi, Jiwei Li

Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders (2026.findings-acl)

Challenge:	Existing RE models are incapable of handling implicit expressions and long-tail relation types due to language complexity and data sparsity.
Approach:	They propose a method to enhance relation extraction using k nearest neighbors (kNN-RE) kNN is a nearest-neighbor search tool that allows the model to consult training relations at test time .
Outcome:	The proposed model outperforms the best model to date on ACE05, SciERC, and Wiki80 datasets and outperformed the best on i2b2 and Wik80 dataset.

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Ailiang Lin, Zhuoyun Li, Keyu Mao, Kotaro Funakoshi, Manabu Okumura

Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation (2023.emnlp-main)

Challenge:	Large language models (LLMs) have been widely explored for embedding generation.
Approach:	They propose an embedding-based in-context prompt training strategy that leverages in-constext learning to generate high-quality embeddables while reducing computational burden.
Outcome:	The proposed method surpasses models trained on publicly available retrieval data and achieves state-of-the-art embedding performance on the MTEB benchmark.

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Jiaang Li, Quan Wang, Yi Liu, Licheng Zhang, Zhendong Mao

Improving Relation Extraction with Knowledge-attention (D19-1)

Challenge:	Existing approaches to learning on Knowledge Graphs (KGs) are not critical for learning on KGs.
Approach:	They propose an alternative approach to represent entities by composing entity-corresponding codewords matched from predefined small-scale codebooks.
Outcome:	The proposed approach achieves similar results to existing methods.

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Pengfei Li, Kezhi Mao, Xuefeng Yang, Qi Li

Learning to Rewrite: Generalized LLM-Generated Text Detection (2025.acl-long)

Challenge:	Existing attention mechanisms are data-driven, but most are data driven.
Approach:	They propose a knowledge-attention encoder which integrates prior knowledge from external lexical resources into deep neural networks for relation extraction task.
Outcome:	The proposed system outperforms existing CNN, RNN, and self-attention based models on a large-scale relation extraction dataset.

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Wei Hao, Ran Li, Weiliang Zhao, Junfeng Yang, Chengzhi Mao

Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation (2026.findings-acl)

Challenge:	Existing detectors for Large Language Models (LLMs) struggle to generalize in open-world settings.
Approach:	They propose a framework to detect LLM-generated text with exceptional generalization to unseen domains by reinforcing LLMs’ inherent rewriting tendencies.
Outcome:	The proposed framework outperforms state-of-the-art detection methods by 23.04% in AUROC, 35.10% for out-of distribution tests, and 48.66% under adversarial attacks.

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Jiaang Li, Zhendong Mao, Quan Wang, Yuning Wan, Yongdong Zhang

DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling (2025.findings-acl)

Challenge:	Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) however, LLMs exhibit a stylistic bias when presented with mixed contexts, revealing a bottleneck in their utility.
Approach:	They propose a style-controlled rewriter that aligns retrieved documents with a question-oriented style while preserving facts.
Outcome:	The proposed model improves RAG pipelines by 8% with negligible latency overhead.

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Minzheng Wang, Xinghua Zhang, Kun Chen, Nan Xu, Haiyang Yu, Fei Huang, Wenji Mao, Yongbin Li

TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)

Challenge:	Large language models (LLMs) enabled dialogue systems are one of the central modes in human-machine interaction.
Approach:	They propose a benchmark task for dialogue element MOdeling and Element Awareness and a new benchmark for dialogue agent interaction that allows the agent to model dialogue elements via imitation learning.
Outcome:	The proposed agent performs well in both dialogue element modeling and out-of-domain tasks.

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Yilun Kong, Jingqing Ruan, YiHong Chen, Bin Zhang, Tianpeng Bao, Shi Shiwei, Du Qing, Xiaoru Hu, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao, Xueqian Wang

Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup (2021.emnlp-main)

Challenge:	Large language models have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools.
Approach:	They propose a framework to enhance the task planning and tool usage abilities of LLMs in industrial systems.
Outcome:	The proposed framework enhances the task planning and tool usage abilities of LLM-based agents in industrial systems.

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Guang Liu, Yuzhao Mao, Huang Hailong, Gao Weiguo, Li Xuan

Empathy Intent Drives Empathy Detection (2023.emnlp-main)

Challenge:	Experiments on five text classification benchmarks and five backbone models have shown that our methods reduce the error rate over Mixup variants in a significant margin (up to 31.3%), especially in low-resource conditions (upto 17.5%).
Approach:	They propose to add a small adversarial perturbation to the mixing coefficients rather than the examples to relax locally linear constraints.
Outcome:	Experiments on five text classification benchmarks and five backbone models show that the proposed methods reduce the error rate over Mixup variants by 31.3%, especially in low-resource conditions.

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Liting Jiang, Di Wu, Bohui Mao, Yanbing Li, Wushour Slamu

Training Language Models to Critique With Multi-agent Feedback (2025.findings-emnlp)

Challenge:	Empathy is essential in human social interaction.
Approach:	They propose to annotate healthy empathy detection datasets IEMPATHIZE and TwittEmp with 8 empathy intent labels and perform joint training for the two tasks.
Outcome:	The proposed framework outperforms baselines on the two datasets.

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Tian Lan, Wenwei Zhang, Chengqi Lyu, Shuaibin Li, Chen Xu, Heyan Huang, Dahua Lin, Xian-Ling Mao, Kai Chen

Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching (2022.findings-naacl)

Challenge:	utilizing human annotations can enhance critique ability, but model-generated critiques suffer from inherent flaws due to complexity of critique . a new framework that leverages multi-agent feedback improves critique ability .
Approach:	They propose a framework that leverages multi-agent feedback to improve critique ability . they propose to use supervised fine-tuning and reinforcement learning to improve this capability .
Outcome:	The proposed framework improves critique ability in both supervised fine-tuning and reinforcement learning stages.

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Kunbo Ding, Weijie Liu, Yuejian Fang, Zhe Zhao, Qi Ju, Xuefeng Yang, Rong Tian, Zhu Tao, Haoyan Liu, Han Guo, Xingyu Bai, Weiquan Mao, Yudong Li, Weigang Guo, Taiqiang Wu, Ningyuan Sun

TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data Synthesis (2025.acl-long)

Challenge:	Existing studies have shown that cross-lingual knowledge distillation can improve the performance of pre-trained models for cross-linguistic similarity matching tasks.
Approach:	They propose a multi-stage distillation framework for constructing a small-size but high-performance cross-lingual model using contrastive learning, bottleneck, and parameter recurrent strategies.
Outcome:	The proposed model can compress the size of XLM-R and MiniLM by more than 50% while the performance is only reduced by about 1%.

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Xiaorui Wu, Xiaofeng Mao, Fei Li, Xin Zhang, Xuanhong Li, Chong Teng, Donghong Ji, Zhuang Li

FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation (2025.acl-long)

Challenge:	Large Language Models (LLMs) excel in natural language processing tasks but are vulnerable to harmful content and being exploited for malicious purposes.
Approach:	They propose a framework to measure the risk coverage of alignment datasets across three dimensions: Lexical Diversity, Malicious Intent, and Jailbreak Tactics.
Outcome:	The proposed framework measures risk coverage across Lexical Diversity, Malicious Intent, and Jailbreak Tactics.

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Wei Li, Xin Zhang, Zhongxin Guo, Shaoguang Mao, Wen Luo, Guangyue Peng, Yangyu Huang, Houfeng Wang, Scarlett Li

Programming Languages in NLP

Challenge:	Existing benchmarks focus on standalone programming problems, such as HumanEval, MBPP, and LiveCodeBench.
Approach:	They propose to use large language models to evaluate their ability to perform incremental development within code repositories by collecting pull requests from 83 GitHub repositorias and using rule-based and intent-based filtering to construct task instances focused on new feature development.
Outcome:	The proposed benchmarks show that large language models perform significantly worse in the FEA-Bench, highlighting considerable challenges in repository-level incremental code development.

MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark (2025.acl-long)

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Qihao Zhao, Yangyu Huang, Tengchao Lv, Lei Cui, Qinzheng Sun, Shaoguang Mao, Xin Zhang, Ying Xin, Qiufeng Yin, Scarlett Li, Furu Wei

Knowledge-Augmented Question Error Correction for Chinese Question Answer System with QuestionRAG (2025.emnlp-industry)

Challenge:	Multiple-choice question datasets like Massive Multitask Language Understanding (MMLU) have inevitably led to benchmark contamination, resulting in unreliable evaluation.
Approach:	They propose a contamination-free MCQ benchmark called MMLU-CF which reassesses LLMs’ understanding of world knowledge by averting both unintentional and malicious data contamination.
Outcome:	The proposed MMLU-CF reassesses LLMs’ understanding of world knowledge by averting both unintentional and malicious data contamination.

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Longpeng Qiu, Ting Li, Shuai Mao, Nan Yang, Xiaohui Yan

Explainability & Interpretability in NLP

Challenge:	Large language models struggle with input errors, often failing to interpret user intent or altering the original question’s structure (over-correction).
Approach:	They propose a framework that uses reinforcement learning to address misinterpretation and over-correction by integrating external knowledge with the input.
Outcome:	The proposed framework unlocks the full potential of LLMs for the question correction task.

Adversarial Preference Learning for Robust LLM Alignment (2025.findings-acl)

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Yuanfu Wang, Pengyu Wang, Chenyang Xi, Bo Tang, Junyi Zhu, Wenqiang Wei, Chen Chen, Chao Yang, Jingfeng Zhang, Chaochao Lu, Yijun Niu, Keming Mao, Zhiyu Li, Feiyu Xiong, Jie Hu, Mingchuan Yang

MTAVG-Bench: A Diagnostic Benchmark for Multi-Talker Dialogue-Centric Audio-Video Generation (2026.acl-long)

Challenge:	Modern language models rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors, but they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation; (2) the vast diversity of potential adversarials; and (3) the risk of feedback bias and reward hacking.
Approach:	They propose an iterative adversarial training method that incorporates three key innovations to address these challenges.
Outcome:	Experiments on Mistral-7B-Instruct-v0.3 show that the proposed method significantly enhances robustness and reduces harmful outputs from 5.88% to 0.43%.

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Yanghao Zhou, Haitian Li, Rexar Lin, Heyan Huang, Jinxing Zhou, Changsen Yuan, Tian Lan, Ziqin Zhou, Yudong Li, Jiajun Xu, Jingyun Liao, YiMing Cheng, Xuefeng Chen, Xian-Ling Mao, Yousheng Feng

CSL: A Large-scale Chinese Scientific Literature Dataset (2022.coling-1)

Challenge:	Existing evaluation benchmarks for text-to-audio-video (T2AV) generation are largely designed for human-recorded videos or single-speaker settings.
Approach:	They propose a failure-driven diagnostic benchmark for multi-talker dialogue-centric audio-video generation.
Outcome:	The benchmark evaluates multi-speaker dialogue generation at four levels: audio-visual signal fidelity, temporal attribute consistency, social interaction, and cinematic expression.

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Yudong Li, Yuqing Zhang, Zhe Zhao, Linlin Shen, Weijie Liu, Weiquan Mao, Hui Zhang

Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning (D19-1)

Challenge:	Existing datasets centered around the English language restrict development of Chinese scientific NLP.
Approach:	They present a large-scale Chinese scientific literature dataset based on Chinese papers . they use semi-structured data as a natural annotation for many supervised NLP tasks .
Outcome:	The proposed dataset can serve as a Chinese corpus and perform many supervised tasks.

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Heng Wang, Shuangyin Li, Rong Pan, Mingzhi Mao

WSpeller: Robust Word Segmentation for Enhancing Chinese Spelling Check (2022.findings-emnlp)

Challenge:	Existing methods for learning knowledge Graphs are incomplete and therefore need well-pretraining.
Approach:	They propose a deep reinforcement learning based model which incorporates LSTM and Graph Attention Mechanism as the memory components.
Outcome:	The proposed model can get rid of the pretraining process and achieve state-of-the-art performance compared with the other models.

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Fangfang Li, Youran Shan, Junwen Duan, Xingliang Mao, Minlie Huang

Debate-Feedback: A Multi-Agent Framework for Efficient Legal Judgment Prediction (2025.naacl-short)

Challenge:	Chinese spelling check (CSC) detects and corrects spelling errors in Chinese texts.
Approach:	They propose a Chinese spelling check model that takes into account word segmentation and a module that can assist the correction module by predicting correct word segmentations from sentences containing spelling errors.
Outcome:	The proposed model outperforms baselines on SIGHAN13, SIGHEN14, and SIGHAN15 and maintains equal performance on SSGHAN14.

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Xi Chen, Mao Mao, Shuo Li, Haotian Shangguan

Green & Sustainable NLP

Challenge:	Comparative experiments show that our model outperforms several general-purpose and domain-specific legal models.
Approach:	They propose a legal judgment prediction model that integrates LLMs with argumentative reasoning techniques to simulate the debate phase of real courtroom trials.
Outcome:	The proposed model outperforms several general-purpose and domain-specific legal models and offers a dynamic reasoning process.

Parameter-efficient Continual Learning Framework in Industrial Real-time Text Classification System (2022.naacl-industry)

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Tao Zhu, Zhe Zhao, Weijie Liu, Jiachi Liu, Yiren Chen, Weiquan Mao, Haoyan Liu, Kunbo Ding, Yudong Li, Xuefeng Yang

Information Retrieval

Challenge:	Existing continual learning methods use data replay, parameter isolation and regularization to mitigate catastrophic forgetting.
Approach:	They propose a parameter-efficient continual learning framework that updates parameters offline and then trains using an online regularization method.
Outcome:	The proposed framework reduces catastrophic forgetting and saves the model with the changed parameters instead of all parameters.

Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries (2022.naacl-main)

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Xiangru Tang, Alexander Fabbri, Haoran Li, Ziming Mao, Griffin Adams, Borui Wang, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev

AutoAlign: Get Your LLM Aligned with Minimal Annotations (2025.acl-demo)

Challenge:	Existing pre-trained summarization models produce text that is factually inconsistent with the input.
Approach:	They present a scale-based scale for Likert rating and a scoring algorithm for Best-Worst Scaling to improve crowdsourcing reliability.
Outcome:	The proposed model is more reliable than existing models on two news summarization datasets.

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Xinyu Lu, Dong Xu, Chunkang Zhang, Xinyan Guan, Junxiang Wang, Qingyu Zhang, Pengbo Wang, Yingzhi Mao, Hao Xiang, Xueru Wen, Zichao Li, Yaojie Lu, Hongyu Lin, Le Sun, Xianpei Han

GPT-RE: In-context Learning for Relation Extraction using Large Language Models (2023.emnlp-main)

Challenge:	Automated Alignment (ALM) is a set of algorithms designed to align Large Language Models (LLMs) with human intentions and values while minimizing manual intervention.
Approach:	They propose an open-source toolkit that integrates mainstream automated algorithms through a consistent interface and an accessible workflow supporting one-click execution for prompt synthesis and automatic alignment signal construction.
Outcome:	The proposed framework enables easy reproduction of existing results through extensive benchmarks and facilitates the development of novel approaches via modular components.

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Zhen Wan, Fei Cheng, Zhuoyuan Mao, Qianying Liu, Haiyue Song, Jiwei Li, Sadao Kurohashi

Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis (2024.eacl-long)

Challenge:	Existing approaches to in-context learning (ICL) are lacking in relation extraction (RE) . emergence of large language models (LLMs) such as GPT-3 represents a significant advancement in natural language processing.
Approach:	They propose to incorporate task-aware representations into demonstration retrieval and enrich the demonstrations with gold label-induced reasoning logic.
Outcome:	The proposed model achieves SOTA and competitive performances on the Semeval and SciERC datasets.

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Zongxia Li, Andrew Mao, Daniel Stephens, Pranav Goel, Emily Walpole, Alden Dima, Juan Fung, Jordan Boyd-Graber

Enhancing Multilingual Reasoning via Steerable Model Merging (2026.findings-acl)

Challenge:	Existing evaluation metrics such as coherence and coherency are inadequate for neural topic models.
Approach:	They conduct the first evaluation of neural, supervised and classical topic models in an interactive task-based setting.
Outcome:	The proposed model performs better on cluster evaluation metrics and human evaluations than classical models on real-world tasks.

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Zhuoran Li, Rui Xu, Jian Yang, Junnan Liu, Zhijun Chen, Qianren Mao, Hongcheng Guo, Jiaheng Liu, Likang Xiao, Ming LI, Xiaojie Wang

Foundation Models Meet Embodied Agents (2025.naacl-tutorial)

Challenge:	Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model.
Approach:	They propose a model merging framework that modulates the contribution of each source model.
Outcome:	Experiments show that the proposed model merging framework outperforms strong baselines on multilingual reasoning benchmarks across 21 different languages.

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Manling Li, Yunzhu Li, Jiayuan Mao, Wenlong Huang

Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text Generation (2024.acl-long)

Challenge:	This tutorial will present a systematic overview of recent advances in foundation models for embodied agents .
Approach:	This tutorial will present a systematic overview of recent advances in foundation models for embodied agents.
Outcome:	This tutorial covers three types of foundation models for embodied agents .

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Tianqi Zhong, Zhaoyi Li, Quan Wang, Linqi Song, Ying Wei, Defu Lian, Zhendong Mao

Lightweight Contenders: Navigating Semi-Supervised Text Mining through Peer Collaboration and Self Transcendence (2025.findings-naacl)

Challenge:	Existing MCTG methods face a noticeable performance drop in compositional testing.
Approach:	They propose a benchmark to evaluate compositional generalization of MCTG methods by combining multi-aspect labeled datasets and a crafted three-dimensional evaluation protocol.
Outcome:	The proposed framework improves compositional generalization performance by 3.64% and 94.4% in compositional testing.

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Qianren Mao, Weifeng Jiang, Junnan Liu, Chenghua Lin, Qian Li, Xianqing Wen, Jianxin Li, Jinhu Lu

TREA: Tree-Structure Reasoning Schema for Conversational Recommendation (2023.acl-long)

Challenge:	Existing frameworks for semi-supervised text mining with lightweight models are limited by label data scarcity.
Approach:	They propose a framework for semi-supervised text mining with lightweight models . it incorporates online distillation to train lightweight student models by imitating the Teacher model .
Outcome:	The proposed framework exhibits notable performance enhancements over existing frameworks.

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Wendi Li, Wei Wei, Xiaoye Qu, Xian-Ling Mao, Ye Yuan, Wenfeng Xie, Dangyang Chen

Grammatical Error Correction via Mixed-Grained Weighted Training (2023.findings-emnlp)

Challenge:	Recent reasoning-based models cannot fully figure out complex causal relationships between mentioned entities with external knowledge.
Approach:	They propose a Tree structure Reasoning schEmA that constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities.
Outcome:	Extensive experiments on two public CRS datasets show the proposed model works.

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Jiahao Li, Quan Wang, Chiwei Zhu, Zhendong Mao, Yongdong Zhang

RAFT: Realistic Attacks to Fool Text Detectors (2024.emnlp-main)

Challenge:	Empirical evaluation shows that MainGEC achieves consistent and significant performance improvements on two benchmark datasets.
Approach:	They propose to use mixed-grained weighted training to improve the training effect for GEC by analyzing the inherent discrepancies in annotated training data.
Outcome:	Empirical results show that the proposed method achieves significant performance improvements on two benchmark datasets.

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James Wang, Ran Li, Junfeng Yang, Chengzhi Mao

Alleviating Hallucinations in Large Language Models via Truthfulness-driven Rank-adaptive LoRA (2025.findings-acl)

Challenge:	Large language models (LLMs) have exhibited remarkable fluency across tasks, but their unethical applications are unclear.
Approach:	They propose a grammar error-free black-box attack that exploits LLM embeddings at the word-level while preserving original text quality.
Outcome:	The proposed attack compromises all detectors across domains and is transferable across source models.

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Jiahao Li, Zhendong Mao, Quan Wang

PAED: Zero-Shot Persona Attribute Extraction in Dialogues (2023.acl-long)

Challenge:	Existing methods to improve truthfulness are training-free without modifying the LLM itself.
Approach:	They propose a rank-adaptive LoRA method to improve LLM truthfulness that allocates ranks according to truthfulness correlations of LLM modules.
Outcome:	The proposed method outperforms state-of-the-art methods on the LLM family and makes the performance of 7B LLMs exceed GPT-4.

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Luyao Zhu, Wei Li, Rui Mao, Vlad Pandelea, Erik Cambria

Low-Resource NLP

Challenge:	Existing methods for persona attribute extraction from conversations are inconsistent and unreliable.
Approach:	They propose a model with a hard negative sampling strategy for generalized zero-shot persona attribute extraction.
Outcome:	The proposed model outperforms existing models in persona attribute extraction tasks.

NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism (2024.acl-long)

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Miao Li, Ming-Bin Chen, Bo Tang, ShengbinHou ShengbinHou, Pengyu Wang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Keming Mao, Cheng Peng, Yi Luo

Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity (2022.emnlp-main)

Challenge:	a novel evaluation framework assesses the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism.
Approach:	They propose to use a benchmark dataset to assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism.
Outcome:	The proposed evaluation framework is based on a dataset of 1,267 test samples in 24 news domains.

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Jiahao Li, Quan Wang, Zhendong Mao, Junbo Guo, Yanyan Yang, Yongdong Zhang

A Silver Bullet or a Compromise for Full Attention? A Comprehensive Study of Gist Token-based Context Compression (2025.acl-long)

Challenge:	Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts.
Approach:	They propose an auxiliary task of Chinese pronunciation prediction to improve CSC . they propose adaptive weighting schemes and a delicate correction strategy .
Outcome:	The proposed auxiliary task improves Chinese pronunciation prediction on three benchmarks.

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Chenlong Deng, Zhisong Zhang, Kelong Mao, Shuaiyi Li, Xinting Huang, Dong Yu, Zhicheng Dou

Challenge:	gist-based context compression methods can achieve only slight performance loss on tasks like retrieval-augmented generation and long-document QA, but it faces challenges in tasks like synthetic recall.
Approach:	They propose two strategies to improve gist-based context compression in large language models.
Outcome:	The proposed methods can achieve only slight performance loss on retrieval-augmented generation and long-document QA tasks, but they face challenges in tasks like synthetic recall.

InfoGain-RAG: Boosting Retrieval-Augmented Generation through Document Information Gain-based Reranking and Filtering (2025.emnlp-main)

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Zihan Wang, Zihan Liang, Zhou Shao, Yufei Ma, Huangyu Dai, Ben Chen, Lingtao Mao, Chenyi Lei, Yuqing Ding, Han Li

Information Retrieval

Challenge:	Retrieval-Augmented Generation (RAG) frameworks struggle with identifying whether retrieved documents meaningfully contribute to answer generation.
Approach:	They propose a document-related metric to quantify the contribution of retrieved documents to correct answer generation.
Outcome:	The proposed framework outperforms existing approaches on both single and multiple retrieval paradigms.

INT: Establishing Information Transfer for Multilingual Intent Detection and Slot Filling (2025.findings-acl)

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Di Wu, Liting Jiang, Bohui Mao, Hongyan Xie, Haoxiang Su, Zhongjiang He, Ruiyu Fang, Shuangyong Song, Hao Huang, Xuelong Li

Sentiment Analysis

Challenge:	Existing studies struggle to achieve performance comparable to that on high-resource languages due to inherent linguistic diversity of multilingual SLU tasks.
Approach:	They propose a multilingual information transfer network to solve these challenges . they propose to reformulate SF as a span prediction problem and introduce a slot-matching attention mechanism to achieve slot alignment across languages.
Outcome:	The proposed model outperforms baseline models on the MASSIVE and MASSIV-UG datasets in overall accuracy across all languages.

MiMoTable: A Multi-scale Spreadsheet Benchmark with Meta Operations for Table Reasoning (2025.coling-main)

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Zheng Li, Yang Du, Mao Zheng, Mingyang Song

Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment (2021.acl-long)

Challenge:	Existing benchmarks for table reasoning are incomplete due to the complexity of the tables and user questions in real-world applications.
Approach:	They propose a Multi-scale spreadsheet benchmark with Meta operations for Table reasoning that incorporates two key features and a new criterion with six categories of meta operations for measuring the difficulty of each question.
Outcome:	The proposed model outperforms Claude-3.5-Sonnet with 77.4% accuracy on the existing benchmarks.

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Zewen Chi, Li Dong, Bo Zheng, Shaohan Huang, Xian-Ling Mao, Heyan Huang, Furu Wei

MetaPro Online: A Computational Metaphor Processing Online System (2023.acl-demo)

Challenge:	Experimental results show that denoising word alignment improves cross-lingual transferability . most applications and resources are still English-centric, making non-English users hard to access.
Approach:	They propose to denoise word alignment as a cross-lingual pre-training task . they first self-label word alignments for parallel sentences and then mask tokens .
Outcome:	The proposed model improves cross-lingual transferability on token-level tasks, especially on question answering, and structured prediction.

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Rui Mao, Xiao Li, Kai He, Mengshi Ge, Erik Cambria

Challenge:	Metaphors do not take literal meanings in contexts, which may cause difficulties for language learners and machines to understand them.
Approach:	They propose a computational metaphor processing online system that queries metaphoricity labels, paraphrases and concept mappings for non-domain-specific text.
Outcome:	The proposed system can query metaphoricity labels, paraphrases, and concept mappings for non-domain-specific text without coding background.

DialogueTRM: Exploring Multi-Modal Emotional Dynamics in a Conversation (2021.findings-emnlp)

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Yuzhao Mao, Guang Liu, Xiaojie Wang, Weiguo Gao, Xuan Li

Extrapolating to Unknown Opinions Using LLMs (2025.coling-main)

Challenge:	Existing studies focus on the self and inter-personal dependencies in multi-modal conversations, but they ignore the temporal and spatial dependencies.
Approach:	They propose a Dialogue Transformer for simultaneously modeling the intra-modal and inter-modal emotion dynamics.
Outcome:	The proposed models outperform the state-of-the-art on three benchmark datasets.

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Kexun Zhang, Jane Dwivedi-Yu, Zhaojiang Lin, Yuning Mao, William Yang Wang, Lei Li, Yi-Chia Wang

RaFe: Ranking Feedback Improves Query Rewriting for RAG (2024.findings-emnlp)

Challenge:	ice cream flavors and climate change are among the topics people hold on various topics.
Approach:	They propose to use a large language model to extrapolate from stances to unknown opinions by prompting and fine-tuning data to improve their ability to extrapole from known to unknown stance.
Outcome:	The proposed model can extrapolate from opinions on known topics to unknown ones and generate reasoning behind extrapolation.

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Shengyu Mao, Yong Jiang, Boli Chen, Xiao Li, Peng Wang, Xinyu Wang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model (2022.naacl-industry)

Challenge:	Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved to enhance document retrieval by reformulating queries.
Approach:	They propose a framework for training query rewriting models that leverages a reranker framework.
Outcome:	The proposed framework provides ranking feedback aligned well with the rewriting objectives without needing signals from annotations and supports both online and offline training models.

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Gongzheng Li, Yadong Xi, Jingzhen Ding, Duan Wang, Ziyang Luo, Rongsheng Zhang, Bai Liu, Changjie Fan, Xiaoxi Mao, Zeng Zhao

Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style Transfer (2021.emnlp-main)

Challenge:	Recent studies show that transformer-based models are effective over many tasks, but they are expensive to deploy in the industrial application.
Approach:	They propose a transformer-based inference solution that optimizes kernels for long inputs and large hidden sizes and a flexible CUDA memory manager to reduce the memory footprint when deploying a large model.
Outcome:	The proposed solution achieves an average speedup of 1.40-4.20x on the transformer decoder layer with an A100 GPU.

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Yun Ma, Yangbin Chen, Xudong Mao, Qing Li

Low-Resource NLP

Challenge:	Existing methods for unsupervised text style transfer struggle to achieve high style conversion rate and low content loss.
Approach:	They propose a collaborative learning framework for unsupervised text style transfer using a pair of bidirectional decoders.
Outcome:	The proposed framework achieves strong empirical results on style compatibility and content preservation.

Representation Alignment and Adversarial Networks for Cross-lingual Dependency Parsing (2024.findings-emnlp)

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Ying Li, Jianjian Liu, Zhengtao Yu, Shengxiang Gao, Yuxin Huang, Cunli Mao

STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension (2022.emnlp-main)

Challenge:	Pre-trained language models have improved dependency parsing accuracy in resource-rich languages . however, the accuracy drops sharply when the model is transferred to low-resource language .
Approach:	They propose a representation alignment and adversarial model to filter out useful knowledge from rich-resource language and ignore useless ones.
Outcome:	The proposed model outperforms baseline models on the benchmark datasets by 1.37 LAS and 1.34 UAS.

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Borui Wang, Chengcheng Feng, Arjun Nair, Madelyn Mao, Jai Desai, Asli Celikyilmaz, Haoran Li, Yashar Mehdad, Dragomir Radev

SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation (2026.findings-acl)

Challenge:	Abstractive dialogue summarization is an important standalone task in natural language processing, but no previous work has explored whether it can be used to boost an NLP system's performance on other important dialogue comprehension tasks.
Approach:	They propose a novel type of dialogue summarization task that decomposes and imitates the hierarchical, systematic and structured mental process that human beings usually go through when understanding and analyzing dialogues.
Outcome:	The proposed model improves the performance of transformer encoder language models on two important dialogue comprehension tasks.

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Wenjie Yang, Mao Zheng, Mingyang Song, Zheng Li, Sitong Wang

Machine Translation

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities in machine translation, but most MT-specific LLMs rely heavily on external supervision during training.
Approach:	They propose a reinforcement learning framework for machine translation that is reference-free and relies solely on self-judging rewards.
Outcome:	The proposed framework outperforms existing LLMs and larger general LLM models on English Chinese translation benchmarks and performs competitively with leading closed-source systems.

The Digital Dunning-Kruger Effect: Decoupling Hallucinations via Geometric Hidden-state Observation for Semantic Truthfulness (2026.acl-long)

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Yueheng Mao, Min Yu, Gengwang Li, Jianguo Jiang, Gang Li, Meng Zhang, Zhen Xu, Weiqing Huang, Ming Liu

A Simple Temporal Information Matching Mechanism for Entity Alignment between Temporal Knowledge Graphs (2022.coling-1)

Challenge:	Large Language Models (LLMs) often generate overconfident yet factually incorrect hallucinations.
Approach:	They propose a black-box-based framework that captures stubborn hallucinations by integrating internal geometric dynamics with output probability distributions.
Outcome:	The proposed framework outperforms white-box methods and reduces computational overhead by over 90%.

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Li Cai, Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, Man Lan

Voice Query Auto Completion (2021.emnlp-main)

Challenge:	Existing methods for EA between temporal KGs incorporate relational and temporal information into entity embeddings.
Approach:	They propose a method to generate unsupervised alignment seeds using temporal information from TKGs.
Outcome:	The proposed method outperforms the previous methods by using temporal information.

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Raphael Tang, Karun Kumar, Kendra Chalkley, Ji Xin, Liming Zhang, Wenyan Li, Gefei Yang, Yajie Mao, Junho Shin, Geoffrey Craig Murray, Jimmy Lin

From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning (2026.findings-acl)

Challenge:	Existing methods fail to complete voice queries from incomplete prefixes because they use orthographic prefix and substrings instead of the true phonetic prefix.
Approach:	They propose to condition QAC approaches on intermediate transcriptions to complete voice queries.
Outcome:	The proposed method obtains an 18% relative improvement over previous methods on a speech-enabled smart television with real-life voice search traffic.

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Beining Wu, Fuyou Mao, Jiong Lin, Cheng Yang, Jiaxuan Lu, Yifu Guo, Siyu Zhang, Yifan Wu, Ying Huang, Fu Li

Challenge:	Generative engines (GEs) are replacing ranked links with citation-grounded answers . current methods are unable to accumulate or transfer effective strategies across tasks and engines .
Approach:	They propose a multi-agent framework where planning, editing, and fidelity-aware evaluation serve as the execution layer.
Outcome:	The proposed framework outperforms heuristic baselines in visibility and citation fidelity on three mainstream engines.

DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining (2023.emnlp-main)

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Weifeng Jiang, Qianren Mao, Chenghua Lin, Jianxin Li, Ting Deng, Weiyi Yang, Zheng Wang

InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training (2021.naacl-main)

Challenge:	Existing text mining models are fine-tuned by fine-timing a large pre-trained language model (PLM) in downstream tasks.
Approach:	They propose a semi-supervised learning framework for fine-tuning a cohort of small student models generated from a large pre-trained language model using knowledge distillation.
Outcome:	The proposed framework outperforms baseline models on semi-supervised text classification and extractive summarization tasks while maintaining comparable performance.

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Zewen Chi, Li Dong, Furu Wei, Nan Yang, Saksham Singhal, Wenhui Wang, Xia Song, Xian-Ling Mao, Heyan Huang, Ming Zhou

KuiLeiXi: a Chinese Open-Ended Text Adventure Game (2021.acl-demo)

Challenge:	Existing methods for learning cross-lingual representations are lacking in the field of NLP.
Approach:	They propose a framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts.
Outcome:	The proposed approach improves cross-lingual transferability on benchmarks.

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Yadong Xi, Xiaoxi Mao, Le Li, Lei Lin, Yanjiang Chen, Shuhan Yang, Xuhan Chen, Kailun Tao, Zhi Li, Gongzheng Li, Lin Jiang, Siyan Liu, Zeng Zhao, Minlie Huang, Changjie Fan, Zhipeng Hu