Papers by Chen Duan

Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation (2020.findings-emnlp)

Challenge:	Existing diffusion models fail to address the challenges of generating high-quality images from textual descriptions due to its large vocabulary size and complex character relationships.
Approach:	They propose a framework that integrates Chinese diffusion models with Alibaba Cloud's Platform for AI and enables the generation of contextually relevant images.
Outcome:	The proposed framework integrates with Alibaba Cloud’s Platform for AI, providing accessible and scalable solutions.

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Yiming Xu, Lin Chen, Zhongwei Cheng, Lixin Duan, Jiebo Luo

Ada-LEval: Evaluating long-context LLMs with length-adaptable benchmarks (2024.naacl-long)

Challenge:	Existing approaches to visual question answering (VQA) are not suitable for real-world applications.
Approach:	They propose a supervised multi-modal domain adaptation method for visual question answering in images that exploits supervised domain adaptation.
Outcome:	The proposed method outperforms state-of-the-art methods on the benchmark VQA 2.0 and VizWiz datasets.

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Chonghua Wang, Haodong Duan, Songyang Zhang, Dahua Lin, Kai Chen

Challenge:	Existing long-text evaluation benchmarks, such as L-Eval and LongBench, focus on QA and summarization tasks.
Approach:	They propose a length-adaptable benchmark for evaluating the long-context understanding of large language models.
Outcome:	The proposed benchmarks do not cover ultralong settings (100k+ tokens) and are difficult to evaluate across different length ranges.

ORPP: Self-Optimizing Role-playing Prompts to Enhance Language Model Capabilities (2025.emnlp-main)

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Yifan Duan, Yihong Tang, Kehai Chen, Liqiang Nie, Min Zhang

Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review (2025.findings-acl)

Challenge:	Existing research has explored model-driven strategies for prompt optimization, but these methods suffer from high computational overhead or require strong optimization capabilities from the model itself, which limits their broad applicability.
Approach:	They propose a framework that optimizes and generates role-playing prompts by limiting the prompt search space to role-player scenarios.
Outcome:	The proposed framework matches and surpasses existing prompt optimization methods in terms of performance.

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Pei Fu, Tongkun Guan, Zining Wang, Zhentao Guo, Chen Duan, Hao Sun, Boming Chen, Qianyi Jiang, Jiayao Ma, Kai Zhou, Junfeng Luo

DNN-driven Gradual Machine Learning for Aspect-term Sentiment Analysis (2021.findings-acl)

Challenge:	Recent advances in vision-language models have unified perception and understanding tasks within Visual Question Answering paradigms.
Approach:	They propose to outline timeline, architecture, and pipeline of nearly all TIU MLLMs and review their performance on mainstream benchmarks.
Outcome:	The proposed models perform well on mainstream benchmarks and are compared with other models.

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Murtadha Ahmed, Qun Chen, Yanyan Wang, Youcef Nafa, Zhanhuai Li, Tianyi Duan

Structured Data in NLP

Challenge:	Existing methods for Aspect-Term Sentiment Analysis (ATSA) use pre-specified lexicons to extract sentiment features.
Approach:	They propose a Deep Neural Network-driven approach for Aspect-Term Sentiment Analysis (ATSA) that leverages shared features between labeled and unlabeled instances for knowledge conveyance.
Outcome:	The proposed approach consistently achieves state-of-the-art performance on real benchmark data.

Question Generation from SQL Queries Improves Neural Semantic Parsing (D18-1)

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Daya Guo, Yibo Sun, Duyu Tang, Nan Duan, Jian Yin, Hong Chi, James Cao, Peng Chen, Ming Zhou

Text Generation

Challenge:	Using question generation, we learn a semantic parser with 30% of the supervised training data.
Approach:	They propose to use question generation to learn a semantic parser with less supervised training data.
Outcome:	The proposed method improves the state-of-the-art model with less training data.

CCTC: A Cross-Sentence Chinese Text Correction Dataset for Native Speakers (2022.coling-1)

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Baoxin Wang, Xingyi Duan, Dayong Wu, Wanxiang Che, Zhigang Chen, Guoping Hu

Syntactic Text Processing

Challenge:	Chinese text correction datasets focus on detecting and correcting Chinese spelling errors and grammatical errors.
Approach:	They propose a Chinese text correction dataset for native speakers . they manually annotated 1,500 Chinese texts written by native speakers.
Outcome:	The proposed dataset can detect and correct Chinese spelling errors and grammatical errors.

An Evaluation Resource for Grounding Translation Errors (2025.findings-emnlp)

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Sujin Chen, Kang Wang, Zixuan Zhou, Xiangyu Duan, Wanqun Zhang, Hao Yang, Jinsong Su, Min Zhang

Explainability & Interpretability in NLP

Challenge:	Current fine-grained error analyses do not ground the errors to the reasons why the annotated text spans are erroneous.
Approach:	They use a bi-directional grounding scheme to ground erroneous text in two directions . if the error spans of both directions are consistent, the explanation is valid .
Outcome:	The proposed grounding process improves translation error detection significantly.

Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences (2020.acl-main)

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Xiangyu Duan, Baijun Ji, Hao Jia, Min Tan, Min Zhang, Boxing Chen, Weihua Luo, Yue Zhang

Multilinguality

Challenge:	a monolingual speaker can learn to translate by looking up a bilingual dictionary . a novel task of machine translation (MT) is based on no parallel sentences but can refer to a ground-truth bilingual dictionary and large-scale monolingual corpora.
Approach:	They propose a task of machine translation that uses a bilingual dictionary and large-scale monolingual corpora to translate a monolingual speaker.
Outcome:	The proposed task is based on a bilingual dictionary and large scale monolingual corpora, while being independent on parallel sentences.

A Deep Relevance Model for Zero-Shot Document Filtering (P18-1)

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Chenliang Li, Wei Zhou, Feng Ji, Yu Duan, Haiqing Chen

Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention (P19-1)

Challenge:	Existing methods for document classification do not consider document filtering . existing methods do not include document filter.
Approach:	They propose a deep relevance model for zero-shot document filtering called DAZER . they use word embeddings to extract the relevance signals from word embeds .
Outcome:	The proposed model outperforms existing models on two document collections . it estimates the relevance between a document and a category by using seed words .

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Xiangyu Duan, Mingming Yin, Min Zhang, Boxing Chen, Weihua Luo

Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis (2022.emnlp-main)

Challenge:	Abstractive Sentence Summarization (ASSUM) is a monolingual task that focuses on grasping the core idea of the source sentence and presenting it as the summary.
Approach:	They propose to use monolingual ASSUM to train a cross-lingual ASL system . they propose to train the system on summary word generation and attention .
Outcome:	Experiments show that the proposed method improves on the monolingual ASSUM task.

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Shuai Fan, Chen Lin, Haonan Li, Zhenghao Lin, Jinsong Su, Hang Zhang, Yeyun Gong, JIan Guo, Nan Duan

Sentiment Analysis

Challenge:	Existing pre-trained language representation models (PLMs) capture sentiment information from word-level while under-considering sentence-level information.
Approach:	They propose a Sentiment-aware pre-trained language model with combined Word-level and Sentence-level Pre-training tasks that enhance the PLM’s knowledge about sentiment words.
Outcome:	The proposed model achieves state-of-the-art on various sentence-level and aspect-level sentiment classification benchmarks.

A Table-to-Text Framework with Heterogeneous Multidominance Attention and Self-Evaluated Multi-Pass Deliberation (2023.findings-emnlp)

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Xi Chen, Xinjiang Lu, Haoran Xin, Wenjun Peng, Haoyang Duan, Feihu Jiang, Jingbo Zhou, Hui Xiong

Structured Data in NLP

Challenge:	Table-to-text works have been widely applied in different domains, such as weather forecast and financial report generation.
Approach:	They propose a table-to-text approach on top of Self-evaluated multi-pass Generation and Heterogenous Multidominance Attention to explore the hierarchical structure.
Outcome:	The proposed method outperforms several SOTA methods quantitatively and qualitatively on three public datasets.

Forget the Unneeded: Backdooring Large Language Models via Contrastive-enhanced Machine Unlearning (2025.findings-emnlp)

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Shiji Yang, Shu Zhao, Congyao Mei, Zhen Yang, Jie Chen, Fulan Qian, Zhen Duan, Yanping Zhang

Robustness in NLP

Challenge:	Existing methods for prompt tuning for Large Language Models find backdoor attacks to be significant in data-rich scenarios.
Approach:	They propose a backdoor attacks through contrastive-enhanced machine unlearning in data-limited scenarios . they use a machine un learning method to capture precise backdoor patterns .
Outcome:	The proposed method captures precise backdoor patterns without association between triggers and backdoors, reducing side effects.

Document Segmentation Matters for Retrieval-Augmented Generation (2025.findings-acl)

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Zhitong Wang, Cheng Gao, Chaojun Xiao, Yufei Huang, Shuzheng Si, Kangyang Luo, Yuzhuo Bai, Wenhao Li, Tangjian Duan, Chuancheng Lv, Guoshan Lu, Gang Chen, Fanchao Qi, Maosong Sun

Syntactic Text Processing

Challenge:	Existing rule-based chunking methods lead to suboptimal splits, where overly large chunks introduce irrelevant information and small chunks lack semantic coherence.
Approach:	They propose a method that leverages document summaries as pseudo-instructions to guide chunking by computing semantic similarity between sentences and the summary.
Outcome:	Experiments on multiple open-domain question-answering benchmarks show that PIC significantly improves retrieval accuracy (Hits@k) and end-to-end QA performance (Exact Match) without any additional training.

Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)

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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan

ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training (2020.findings-emnlp)

Challenge:	Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems.
Approach:	They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations.
Outcome:	The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods.

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Weizhen Qi, Yu Yan, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou

Revisiting the Self-Consistency Challenges in Multi-Choice Question Formats for Large Language Model Evaluation (2024.lrec-main)

Challenge:	Existing sequence-to-sequence models are optimized for future n-gram prediction and n stream self-attention mechanism.
Approach:	They propose a self-supervised objective called future n-gram prediction and the proposed n stream self-attention mechanism to optimize the model for sequence-to-sequence learning.
Outcome:	The proposed model achieves state-of-the-art on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks compared to the models using the same scale pre-training corpus.

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Wenjie Zhou, Qiang Wang, Mingzhou Xu, Ming Chen, Xiangyu Duan

BotChat: Evaluating LLMs’ Capabilities of Having Multi-Turn Dialogues (2024.findings-naacl)

Challenge:	Multi-choice questions (MCQs) are a common method for assessing the world knowledge of large language models.
Approach:	They propose three knowledge-equivalent question variants to assess LLMs' world knowledge . they propose option position shuffle, option label replacement, and conversion to a True/False format .
Outcome:	The proposed questions are shuffle, label replacement, and True/False format.

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Haodong Duan, Jueqi Wei, Chonghua Wang, Hongwei Liu, Yixiao Fang, Songyang Zhang, Dahua Lin, Kai Chen

Dialogue Systems & Conversational Agents

Challenge:	Modern Large Language Models (LLMs) facilitate high-quality, multi-turn dialogues with humans, but human-based evaluation of such a capability requires substantial manual effort.
Approach:	They propose to evaluate LLMs' ability to emulate human-like, multi-turn conversations using an LLM-centric approach.
Outcome:	The proposed model emulates human-like, multi-turn conversations using an LLM-centric approach.

Friendly Topic Assistant for Transformer Based Abstractive Summarization (2020.emnlp-main)

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Zhengjue Wang, Zhibin Duan, Hao Zhang, Chaojie Wang, Long Tian, Bo Chen, Mingyuan Zhou

Information Extraction & Text Mining

Challenge:	Abstractive document summarization is a comprehensive task in natural language processing.
Approach:	They propose a topic assistant that rearranges and learns document semantics . they propose TA that is compatible with Transformer-based models and user-friendly .
Outcome:	The proposed model is compatible with Transformer-based models and user-friendly.

Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization (2024.findings-emnlp)

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Yixin Ji, Yang Xiang, Juntao Li, Qingrong Xia, Zi Ye, Xinyu Duan, Zhefeng Wang, Kehai Chen, Min Zhang

InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model (2025.findings-acl)

Challenge:	Large language models require a balance between efficiency and performance.
Approach:	They propose a low-rank compression technique that reduces non-essential parameters by decomposing weight matrices into products of two low-ranked matrici.
Outcome:	The proposed method outperforms existing pruning and low-rank compression techniques in maintaining model performance at the same compression ratio.

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Yuhang Zang, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Ziyu Liu, Shengyuan Ding, Shenxi Wu, Yubo Ma, Haodong Duan, Wenwei Zhang, Kai Chen, Dahua Lin, Jiaqi Wang

Code Execution with Pre-trained Language Models (2023.findings-acl)

Challenge:	Despite the promising performance of Large Vision Language Models, they sometimes generate incorrect outputs.
Approach:	They propose a multi-modal reward model that aligns LVLMs with human preferences.
Outcome:	The proposed model achieves excellent results on the latest multi-modal reward model benchmark and shows competitive performance on text-only reward model.

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Chenxiao Liu, Shuai Lu, Weizhu Chen, Daxin Jiang, Alexey Svyatkovskiy, Shengyu Fu, Neel Sundaresan, Nan Duan

Joint Generator-Ranker Learning for Natural Language Generation (2023.findings-acl)

Challenge:	Pre-trained code intelligence models ignore the execution trace and only rely on source code and syntactic structures to understand code execution.
Approach:	They develop a mutation-based data augmentation technique to create a Python dataset and task for code execution that challenges existing models.
Outcome:	The proposed model outperforms existing models on code execution and shows its potential for zero-shot code-to-code search and text-to code generation.

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Weizhou Shen, Yeyun Gong, Yelong Shen, Song Wang, Xiaojun Quan, Nan Duan, Weizhu Chen

Text Generation

Challenge:	Existing methods for text generation train the generator and ranker individually . existing methods neglect the mutual feedback that could enhance the quality of outputs .
Approach:	They propose a joint training algorithm that integrates the generator and ranker in a single framework.
Outcome:	The proposed algorithm surpasses existing methods on four public datasets across three common generation scenarios.

ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)

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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong

OPT-BENCH: Evaluating the Iterative Self-Optimization of LLM Agents in Large-Scale Search Spaces (2026.findings-acl)

Challenge:	Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs).
Approach:	They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models.
Outcome:	The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks.

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Xiaozhe Li, Jixuan Chen, Xinyu Fang, Shengyuan Ding, Haodong Duan, Qingwen Liu, Kai Chen

Information Retrieval

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and tool use, but their ability to continuously refine solutions in response to dynamic environmental feedback remains underexplored.
Approach:	They propose a benchmark to evaluate self-improvement capabilities in large-scale search spaces by combining 20 machine learning tasks with 10 classic NP-hard problems.
Outcome:	The proposed framework emulates human-like cognitive adaptation and operates via a general perception–memory–reasoning loop, iteratively refining solutions based on environmental feedback.

MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark (2024.findings-acl)

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Hongwei Liu, Zilong Zheng, Yuxuan Qiao, Haodong Duan, Zhiwei Fei, Fengzhe Zhou, Wenwei Zhang, Songyang Zhang, Dahua Lin, Kai Chen

Numerical Reasoning

Challenge:	Recent advances in large language models have showcased significant improvements in mathematics, but traditional benchmarks like GSM8k offer a unidimensional perspective.
Approach:	MathBench is a benchmark that rigorously assesses the mathematical capabilities of large language models.
Outcome:	MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills.

ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation (2021.acl-demo)

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Weizhen Qi, Yeyun Gong, Yu Yan, Can Xu, Bolun Yao, Bartuer Zhou, Biao Cheng, Daxin Jiang, Jiusheng Chen, Ruofei Zhang, Houqiang Li, Nan Duan

Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding (2023.emnlp-main)

Challenge:	Existing models for pre-training are not convenient for users to find and set them up.
Approach:	They propose to extend ProphetNet into other domains and languages by pre-training models . they pre-train a cross-lingual generation model ProphetNet-Multi and a Chinese generation model .
Outcome:	The proposed models achieve new state-of-the-art on 10 benchmarks.

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Taolin Zhang, Ruyao Xu, Chengyu Wang, Zhongjie Duan, Cen Chen, Minghui Qiu, Dawei Cheng, Xiaofeng He, Weining Qian

Dialogue is Better Than Monologue: Instructing Meidcal LLMs via Strategic Conversations (2026.findings-eacl)

Challenge:	Existing methods for pre-training KEPLMs with relational triples are difficult to adapt to close domains due to the lack of sufficient domain graph semantics.
Approach:	They propose a Knowledge-enhanced language representation learning framework for various closed domains that captures the implicit graph structure among the entities.
Outcome:	The proposed framework outperforms existing methods for pre-training KEPLMs in closed domains significantly.

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Zijie Liu, Xinyu Zhao, Jie Peng, Jinhao Duan, Zhuangdi Zhu, Qingyu Chen, Kaidi Xu, Xia Hu, Tianlong Chen

Scaling is Not All You Need: Clinical-Oriented Reinforcement Learning Makes Parameter-Efficient Clinical Reasoning (2026.findings-acl)

Challenge:	Existing tuning methods for medical AI models are monologue-based . existing benchmarks are based on licensing exams or research articles .
Approach:	They propose a benchmark to expose limitations of monologue-based tuning for medical AI models . they use a large dialogue dataset to capture stepwise diagnostic reasoning .
Outcome:	The proposed model outperforms monologue-tuned models on a medical question answering task and improves accuracy on standard medical QA benchmarks.

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Chi Liu, Yan Shu, Mengzhuo Chen, Hongming Piao, Zhijian Duan, Derek Li, Bryan Dai

Rhythm Controllable and Efficient Zero-Shot Voice Conversion via Shortcut Flow Matching (2025.acl-long)

Challenge:	Large language models are increasingly used in medicine, but expert-level clinical reasoning remains a high-complexity, high-stakes frontier.
Approach:	They propose to train clinical reasoning models using a Reasoning-Oriented Data Strategy based on topological synthesis and CoT cold-start.
Outcome:	The proposed pipeline outperforms existing models and outperformed the strongest open-source alternatives up to 671B in MedXpertQA.

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Jialong Zuo, Shengpeng Ji, Minghui Fang, Mingze Li, Ziyue Jiang, Xize Cheng, Xiaoda Yang, Chen Feiyang, Xinyu Duan, Zhou Zhao

FastSeq: Make Sequence Generation Faster (2021.acl-demo)

Challenge:	Existing methods focus on disentangling speakers and content, while others focus on preserving the source's prosody.
Approach:	They propose a rhythm-controllable and efficient zero-shot voice conversion model that transforms the source speaker’s timbre into an unseen one while retaining speech content.
Outcome:	The proposed model adapts the linguistic content duration to the desired speaking style, facilitating the transfer of the target speaker’s rhythm.

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Yu Yan, Fei Hu, Jiusheng Chen, Nikhil Bhendawade, Ting Ye, Yeyun Gong, Nan Duan, Desheng Cui, Bingyu Chi, Ruofei Zhang

GLGE: A New General Language Generation Evaluation Benchmark (2021.findings-acl)

Challenge:	Transformer-based models have made tremendous impact in natural language generation, but inference speed is still a bottleneck due to large model size and intensive computing involved in auto-regressive decoding process.
Approach:	They propose an attention cache optimization, an efficient algorithm for detecting repeated n-grams, and an asynchronous generation pipeline with parallel I/O to accelerate sequence generation without loss of accuracy.
Outcome:	The proposed framework can accelerate the sequence generation by 4x to 9x with a simple one-line code change for a set of widely used and diverse models.

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Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi, Hang Zhang, Jian Jiao, Weizhu Chen, Jie Fu, Linjun Shou, Ming Gong, Pengcheng Wang, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Ruofei Zhang, Winnie Wu, Ming Zhou, Nan Duan

TransFace: Unit-Based Audio-Visual Speech Synthesizer for Talking Head Translation (2024.findings-acl)

Challenge:	Multi-task benchmarks focus on a range of Natural Language Understanding (NLU) tasks without considering the Natural Language Generation (NLG) models.
Approach:	They propose a multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks.
Outcome:	The proposed benchmarks are based on GLUE and Su-perGLUE for English and several other languages.

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Xize Cheng, Rongjie Huang, Linjun Li, Zehan Wang, Tao Jin, Aoxiong Yin, Chen Feiyang, Xinyu Duan, Baoxing Huai, Zhou Zhao

Rapid Diffusion: Building Domain-Specific Text-to-Image Synthesizers with Fast Inference Speed (2023.acl-industry)

Challenge:	Existing methods for talking head translation rely on cascading, resulting in delays and cascadic errors.
Approach:	They propose a model for talking head translation, TransFace, which can translate audio-visual speech into audio-visual speech in other languages.
Outcome:	The proposed model can translate audio-visual speech into audio-visual speech in other languages.

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Bingyan Liu, Weifeng Lin, Zhongjie Duan, Chengyu Wang, Wu Ziheng, Zhang Zipeng, Kui Jia, Lianwen Jin, Cen Chen, Jun Huang

RikiNet: Reading Wikipedia Pages for Natural Question Answering (2020.acl-main)

Challenge:	Text-to-Image Synthesis (TIS) aims to generate images based on textual inputs . but, current diffusion-based models lack entity knowledge and low inference speed .
Approach:	They propose a framework for training and deploying latent diffusion models with rich entity knowledge injected and optimized networks.
Outcome:	The proposed framework improves image quality and inference speed and can be used in industrial applications.

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Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Nan Duan

Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy (2023.findings-emnlp)

Challenge:	Using Wikipedia pages to answer open-domain questions remains challenging in natural language understanding.
Approach:	They propose a model which reads Wikipedia pages for natural question answering . it uses a dynamic paragraph dual-attention reader and a cascaded answer predictor .
Outcome:	The proposed model outperforms the human model on the Natural Questions dataset . it achieves 74.3 F1 and 57.9 F1 on long-answer and short-answer tasks .

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Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, Weizhu Chen

Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarization (2026.findings-acl)

Challenge:	Recent work has proposed to improve relevance modeling by having large language models actively involved in retrieval, i.e., to guide retrieval with generation.
Approach:	They propose to have large language models actively involved in retrieval to guide retrieval with generation.
Outcome:	The proposed method synergizes retrieval and generation in an iterative manner, and can generate better results in subsequent iterations.

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Xiaoyong Mei, Tingting Zuo, Da Chen, Guangyu Hu, Xiangyu Wen, Chao Duan, Mingyan Zhang, Fudan Zheng

Dialogue Systems & Conversational Agents

Challenge:	Existing methods for multi-role dialogue summarization favor surface-level imitation of references rather than genuine gains in faithfulness or alignment with human preferences.
Approach:	They propose a framework that couples explicit cognitive-style reasoning with reward-based optimization for multi-role dialogue summarization.
Outcome:	The proposed framework matches strong baselines on ROUGE and BERTScore, while in-depth analysis on SAMSum shows clear gains in factual faithfulness and model-based preference alignment.

XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)

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Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou

Multilinguality

Challenge:	XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios .
Approach:	They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora.
Outcome:	The proposed dataset is labeled in English and includes only natural language understanding tasks.

Forge: Quality-Aware Reinforcement Learning for NP-Hard Optimization in LLMs (2026.findings-acl)

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Xiaozhe Li, Xinyu Fang, Shengyuan Ding, Yang Li, Linyang Li, Haodong Duan, Qingwen Liu, Kai Chen

Reasoning

Challenge:	Existing benchmarks focus on correctness, overlooking optimality . large language models excel at math, coding, logic and puzzles .
Approach:	They propose a framework for training and evaluating Large Language Models on NP-hard optimization problems through quality-aware RLVR.
Outcome:	The proposed framework outperforms existing benchmarks on math, coding, logic and puzzles.

Dense Procedure Captioning in Narrated Instructional Videos (P19-1)

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Botian Shi, Lei Ji, Yaobo Liang, Nan Duan, Peng Chen, Zhendong Niu, Ming Zhou

Linguistic Minimal Pairs Elicit Linguistic Similarity in Large Language Models (2025.coling-main)

Challenge:	Existing models for video dense captioning learn video segments and generate captions without considering transcripts.
Approach:	They propose a model to generate procedure captions from narrated instructional videos . they extract procedures by a cross-modality module and generate captions by encoding video frames and transcripts within each extracted procedure.
Outcome:	The proposed model can extract procedures from narrated instructional videos and generate procedure captions by encoding video frames and transcripts.

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Xinyu Zhou, Delong Chen, Samuel Cahyawijaya, Xufeng Duan, Zhenguang Cai

KVFKT: A New Horizon in Knowledge Tracing with Attention-Based Embedding and Forgetting Curve Integration (2025.coling-main)

Challenge:	a new analysis leverages linguistic minimal pairs to probe the internal linguistic representations of Large Language Models (LLMs).
Approach:	They propose to use linguistic minimal pairs to probe the internal linguistic representations of Large Language Models (LLMs).
Outcome:	The proposed analysis reveals that linguistic similarity is significantly influenced by training data exposure, leading to higher cross-LLM agreement in higher-resource languages.

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Quanlong Guan, Xiuliang Duan, Kaiquan Bian, Guanliang Chen, Jianbo Huang, Zhiguo Gong, Liangda Fang

Representation Learning

Challenge:	Existing knowledge tracing models do not incorporate forgetting features to improve the learning and answering processes.
Approach:	They propose a new approach in knowledge tracing with attention-based embedding and forgetting curve integration using four real-world datasets to test the model.
Outcome:	The proposed model outperforms the existing knowledge tracing models and eliminates the need for artificial engineering features.

Analytical Reasoning of Text (2022.findings-naacl)

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Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, Nan Duan

Reasoning

Challenge:	Existing models with implicit reasoning ability struggle to solve analytical reasoning of text.
Approach:	They propose an approach to analyze text and use it to perform reasoning over it.
Outcome:	The proposed approach outperforms pre-trained models on an analysis of the Law School Admission Test dataset.

Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and Refinement (2025.acl-long)

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Maosongcao Maosongcao, Taolin Zhang, Mo Li, Chuyu Zhang, Yunxin Liu, Conghui He, Haodong Duan, Songyang Zhang, Kai Chen

Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models (2024.findings-naacl)

Challenge:	Existing high-quality human-annotated SFT data is a bottleneck for Large Language Models (LLMs).
Approach:	They propose a two-stage synthetic data generation framework that incorporates World Knowledge Trees and Self-Reflection Refinement to produce high-quality SFT data at scale.
Outcome:	The proposed model fine-tuned on 20K condor-generated samples achieves superior performance compared to instruct model trained with RLHF.

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Jiashuo Sun, Yi Luo, Yeyun Gong, Chen Lin, Yelong Shen, Jian Guo, Nan Duan

Tree-CoT-RT: An Explainable Multi-Path Tree-Guided Chain-of-Thought and Reinforcement Learning Framework for Aspect Sentiment Quad Prediction (2026.findings-acl)

Challenge:	Chain-of-thought (CoT) prompting is a technique to enhance the reasoning abilities of Large language models (LLMs) however, the reasoning chains of demonstrations are observed to be prone to errors, which can lead to incorrect reasoning during inference.
Approach:	They propose an iterative bootstrapping technique to enhance the reasoning abilities of Large language models (LLMs) by generating a series of reasoning steps to obtain the answer, and using the reasoning chains as exemplars to demonstrate the task.
Outcome:	The proposed method improves the performance of Large language models (LLMs) on three reasoning tasks on ten datasets.

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Hao Zhang, Jiahao Wang, Zhenke Duan, Xin Yin, Haichuan Hu, Hualong Chen, null Suyi, Congqing He, Yike Tan, Yu-N Cheah

Gold-Medal-Level Olympiad Geometry Solving with Efficient Heuristic Auxiliary Constructions (2026.acl-long)

Challenge:	Existing methods lack explainability and generalization, making it difficult to justify inference decisions and detect implicit sentiment across domains and varied expression patterns.
Approach:	They propose an explainable multi-path tree-guided chain-of-thought framework specifically designed for ASQP.
Outcome:	Experiments on benchmark datasets show that Tree-CoT-RT outperforms baselines.

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Boyan Duan, Xiao Liang, Shuai Lu, Yaoxiang Wang, Yelong Shen, Kai-Wei Chang, Ying Nian Wu, Mao Yang, Weizhu Chen, Yeyun Gong

Green & Sustainable NLP

Challenge:	Existing methods for geometry theorem proving in Euclidean geometry are challenging and require a neural network to perform.
Approach:	They propose a method for adding auxiliary points in geometry that runs on CPUs without relying on neural network-based inference.
Outcome:	The proposed method achieves silver-medal-level human performance on IMO-30 benchmark.

Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding (2026.acl-long)

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Sensen Gao, Shanshan Zhao, Xu Jiang, Lunhao Duan, Yong Xien Chng, Qing-Guo Chen, Weihua Luo, Kaifu Zhang, Jia-Wang Bian, Mingming Gong

Information Retrieval

Challenge:	Document understanding is critical for applications from financial analysis to scientific discovery.
Approach:	They propose a taxonomy based on domain, retrieval modality, and granularity and review advances involving graph structures and agentic frameworks.
Outcome:	The proposed model enables holistic retrieval and reasoning across all modalities, unlocking comprehensive document intelligence.

Redundancy Principles for MLLMs Benchmarks (2025.acl-long)

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Zicheng Zhang, Xiangyu Zhao, Xinyu Fang, Chunyi Li, Xiaohong Liu, Xiongkuo Min, Haodong Duan, Kai Chen, Guangtao Zhai

AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models (2024.findings-naacl)

Challenge:	Rapid growth of Multi-modality Large Language Models has led to significant redundancy among benchmarks.
Approach:	They propose a framework to improve MLLM benchmark design by identifying redundancy at three levels: dimension, instance, and cross-benchmark redundancies.
Outcome:	The proposed framework streamlines evaluations and enhances reliability.

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Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, Nan Duan

Reasoning

Challenge:	Traditional benchmarks for evaluating foundation models often fail to accurately represent their general abilities for human-centric tasks.
Approach:	They propose a bilingual benchmark to assess foundation models in the context of human-centric standardized exams such as college entrance exams, law school admission tests, and math competitions.
Outcome:	The proposed benchmark exceeds the average human performance on SAT, LSAT, and math competitions with 95% accuracy and 92.5% on the Chinese college entrance English exam.

Execution as Verification: Fine-Grained Self-Correcting Reasoning for Complex KBQA (2026.acl-long)

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Minghan Zhang, Zhen Yang, Haodong Zou, Jie Chen, Zhen Duan, Shu Zhao

CodeRetriever: A Large Scale Contrastive Pre-Training Method for Code Search (2022.emnlp-main)

Challenge:	Existing knowledge base question answering methods are limited by syntactic constraints and are prone to structural deviations that render queries unexecutable.
Approach:	They propose a framework that reframes semantic parsing as an iterative reasoning process driven by execution feedback.
Outcome:	The proposed method achieves significant improvements in query executability and answer accuracy on the WebQSP and CWQ datasets.

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Xiaonan Li, Yeyun Gong, Yelong Shen, Xipeng Qiu, Hang Zhang, Bolun Yao, Weizhen Qi, Daxin Jiang, Weizhu Chen, Nan Duan

Soft-Labeled Contrastive Pre-Training for Function-Level Code Representation (2022.findings-emnlp)

Challenge:	Existing code pre-training approaches often adopt (masked) language modeling as the training objective which targets on learning to predict (macked) tokens in a given code context.
Approach:	They propose a code-text contrastive learning model which learns function-level code semantic representations through large-scale code corpus.
Outcome:	The proposed model achieves new state-of-the-art with significant improvement over existing pre-trained models on eleven domain/language-specific code search tasks with six programming languages in different code granularity.

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Xiaonan Li, Daya Guo, Yeyun Gong, Yun Lin, Yelong Shen, Xipeng Qiu, Daxin Jiang, Weizhu Chen, Nan Duan

ReTA: Recursively Thinking Ahead to Improve the Strategic Reasoning of Large Language Models (2024.naacl-long)

Challenge:	Existing methods for contrastive pre-training ignore the relevance between codes in large code corpus.
Approach:	They propose a Soft-labeled contrastive pre-training framework with positive sample construction methods to learn functional-level code representation.
Outcome:	The proposed framework can obtain fine-grained soft-labels through an iterative adversarial manner and use them to learn better code representation.

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Jinhao Duan, Shiqi Wang, James Diffenderfer, Lichao Sun, Tianlong Chen, Bhavya Kailkhura, Kaidi Xu

GuideLLM: Exploring LLM-Guided Conversation with Applications in Autobiography Interviewing (2025.naacl-long)

Challenge:	Existing logical reasoning evaluations of Large Language Models (LLMs) focus on single-turn and static environments, such as arithmetic problems.
Approach:	They propose a Recursively Thinking-Ahead agent that analyzes the opponents’ future moves/actions and assigns reward signals for these situations.
Outcome:	The proposed agent is based on two scenarios: Online Racing and Offline Probing.

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Jinhao Duan, Xinyu Zhao, Zhuoxuan Zhang, Eunhye Grace Ko, Lily Boddy, Chenan Wang, Tianhao Li, Alexander Rasgon, Junyuan Hong, Min Kyung Lee, Chenxi Yuan, Qi Long, Ying Ding, Tianlong Chen, Kaidi Xu

Competition-Level Problems are Effective LLM Evaluators (2024.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated their effectiveness in human-guided dialogues, but tasks in the real world are more complex and require greater autonomy from LLMs.
Approach:	They propose to characterize LLM-guided conversation into three fundamental components: Goal Navigation, Context Management, Empathetic Engagement and implement an interviewing environment for the evaluation of LLMs.
Outcome:	The proposed LLM outperforms baseline LLMs in interviewing quality and autobiography generation quality.

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Yiming Huang, Zhenghao Lin, Xiao Liu, Yeyun Gong, Shuai Lu, Fangyu Lei, Yaobo Liang, Yelong Shen, Chen Lin, Nan Duan, Weizhu Chen

Thinking Beyond the Local: Multi-View Instructed Adaptive Reasoning in KG-Enhanced LLMs (2026.findings-eacl)

Challenge:	Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about their capabilities and the potential data contamination problem.
Approach:	They propose to evaluate the reasoning capabilities of large language models in solving recent competition-level programming problems in Codeforces.
Outcome:	The proposed model has experienced a cliff-like decline in problems after September 2021, which shows the potential data contamination and the challenges for any existing LLM to solve unseen complex reasoning problems.

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Minghan Zhang, Shu Zhao, Zhen Yang, Hongsheng Wu, Yongxing Lin, Haodong Zou, Jie Chen, Zhen Duan

Knowledge Representation

Challenge:	Existing methods for large language models adopt query-driven iterative reasoning from a local perspective, limiting efficiency and accuracy for complex multi-hop tasks.
Approach:	They propose a multi-view instructed adaptive reasoning of LLM on Knowledge Graphs that allows LLMs to plan, evaluate, and adapt reasoning paths from a global perspective.
Outcome:	The proposed model overcomes the limitations of local exploration by enabling LLMs to plan, evaluate, and adapt reasoning paths from a global perspective.

EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering (2021.acl-long)

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Zhibin Duan, Hao Zhang, Chaojie Wang, Zhengjue Wang, Bo Chen, Mingyuan Zhou

Information Extraction & Text Mining

Challenge:	Existing studies have shown that data diversity affects the performance of LMs if we train a single LM over the entire dataset.
Approach:	They propose an autoencoding topic model with a mixture prior to perform clustering for the data.
Outcome:	The proposed model can learn knowledge from different samples while extracting cluster-specific features.

A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code (2026.findings-acl)

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Keke Lian, Wang Bin, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Miaoqian Lin, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Quan Jiazheng, Yilu Zhong, Chenhao He, Chen Zichuan, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui LI, Dong Zhang

Programming Languages in NLP

Challenge:	Existing security evaluation benchmarks lack relevance to real-world AI programming tasks . current LLMs struggle with secure coding, research shows .
Approach:	They propose a repository-level evaluation benchmark to assess security of AI-generated code.
Outcome:	The proposed framework mirrors real-world AI programming tasks and offers valuable insights into the state of AI code generation.

ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs (2024.findings-emnlp)

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Jingming Zhuo, Songyang Zhang, Xinyu Fang, Haodong Duan, Dahua Lin, Kai Chen

Graph Neural News Recommendation with Unsupervised Preference Disentanglement (2020.acl-main)

Challenge:	Recent research has neglected instances-level prompt variations and their implications on subjective evaluations.
Approach:	They propose a framework to evaluate and comprehend prompt sensitivity in large language models.
Outcome:	The proposed framework evaluates and comprehends prompt sensitivity in large language models.

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Linmei Hu, Siyong Xu, Chen Li, Cheng Yang, Chuan Shi, Nan Duan, Xing Xie, Ming Zhou

DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)

Challenge:	Existing methods to learn informative user and news representations fail to consider high-order connectivity underlying the user-news interactions.
Approach:	They propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement which can encode high-order relationships into user and news representations by information propagation along the graph.
Outcome:	The proposed model can encode high-order relationships into user and news representations by information propagation along the graph and disentangle latent preference factors by a neighborhood routing algorithm.

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Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan

Dialogue Systems & Conversational Agents

Challenge:	Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP).
Approach:	They propose a dialog pre-training framework that introduces latent variables into the enhanced encoder-decoder pre-train framework to increase relevance and diversity of responses.
Outcome:	The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets.

OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human Preference (2025.acl-long)

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Xiangyu Zhao, Shengyuan Ding, Zicheng Zhang, Haian Huang, Maosongcao Maosongcao, Jiaqi Wang, Weiyun Wang, Xinyu Fang, Wenhai Wang, Guangtao Zhai, Hua Yang, Haodong Duan, Kai Chen