Papers by Nan Xu

LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval (2022.findings-acl)

Challenge:	Existing approaches to decode open-ended text have addressed degeneration problems in large-scale language models (LLMs)
Approach:	They propose an improved decoding algorithm that leverages the Kullback–Leibler divergence to track the distribution distance between current and historical decoding steps.
Outcome:	The proposed algorithm outperforms existing methods in document continuation and story generation.

Copied to clipboard

Canwen Xu, Daya Guo, Nan Duan, Julian McAuley

ImaRA: An Imaginative Frame Augmented Method for Low-Resource Multimodal Metaphor Detection and Explanation (2025.findings-naacl)

Challenge:	Experimental results show that LaPraDoR is state-of-the-art compared with supervised dense retrieval models.
Approach:	They propose a pretrained dual-tower dense retriever that does not require supervised data for training.
Outcome:	The proposed method achieves state-of-the-art performance on 18 datasets of 9 zero-shot text retrieval tasks.

Copied to clipboard

Yuan Tian, Minzheng Wang, Nan Xu, Wenji Mao

RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework (2025.acl-long)

Challenge:	Existing methods for multimodal metaphor detection neglect cross-domain and attribute similarity characteristics underlying multimodal understanding.
Approach:	They propose an Imaginative FRame Augmented method for multimodal metaphor detection and explanation . they use a cross-modal imagination dataset rich in multimodal multimodal expressions .
Outcome:	The proposed method outperforms existing methods with training data on two datasets.

Copied to clipboard

Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu, Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, Maosong Sun

CoSQA: 20,000+ Web Queries for Code Search and Question Answering (2021.acl-long)

Challenge:	Existing evaluation metrics for RAG systems are lacking due to high costs of data construction and lack of factual accuracy.
Approach:	They propose a framework to evaluate RAG systems in specialized scenarios . they propose three new metrics to evaluate LLM-generated responses .
Outcome:	The proposed framework outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.

Copied to clipboard

Junjie Huang, Duyu Tang, Linjun Shou, Ming Gong, Ke Xu, Daxin Jiang, Ming Zhou, Nan Duan

Information Retrieval

Challenge:	Using deep neural networks to find codes is difficult . we present a dataset that includes 20,604 labels for natural language queries and codes .
Approach:	They introduce a contrastive learning method to enhance text-code matching . they find that CoSQA improves the accuracy of code question answering by 5.1% .
Outcome:	The proposed method improves the accuracy of code question answering by 5.1% and improves by 10.5% on a CodeBERT model.

Modeling Conceptual Attribute Likeness and Domain Inconsistency for Metaphor Detection (2023.emnlp-main)

Copied to clipboard

Yuan Tian, Nan Xu, Wenji Mao, Daniel Zeng

Linguistics & Cognitive NLP

Challenge:	Metaphor detection aims to distinguish between metaphorical and literal expressions in text.
Approach:	They propose an attribute likeness and domain inconsistency learning framework for wordpair metaphor detection based on conceptual metaphor theory . they model attribute likeity with an attribute siamese network and devise a domain contrastive learning strategy to learn semantic inconsistentness of concepts in source and target domains .
Outcome:	The proposed framework outperforms existing word-pair and token-level methods on four datasets.

ContextBLIP: Doubly Contextual Alignment for Contrastive Image Retrieval from Linguistically Complex Descriptions (2024.findings-acl)

Copied to clipboard

Honglin Lin, Siyu Li, Guoshun Nan, Chaoyue Tang, Xueting Wang, Jingxin Xu, Rong Yankai, Zhouzhili Zhouzhili, Yutong Gao, Qimei Cui, Xiaofeng Tao

Visual Data in NLP

Challenge:	Existing approaches to image retrieval from contextual descriptions (IRCD) lag behind human performance in IRCD.
Approach:	They propose a method that relies on a doubly contextual alignment scheme for challenging IRCD.
Outcome:	The proposed method can yield comparable results with GPT-4V, despite fewer parameters.

Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data (2023.emnlp-main)

Copied to clipboard

Canwen Xu, Daya Guo, Nan Duan, Julian McAuley

Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinking (2024.findings-naacl)

Challenge:	Despite the promising potential of chat models, they are only accessible through restricted APIs, creating barriers for new research and progress in the field.
Approach:	They propose a pipeline that can automatically generate a high-quality multi-turn chat corpus by leveraging ChatGPT to engage in a conversation with itself.
Outcome:	The proposed pipeline generates a high-quality multi-turn chat corpus by leveraging ChatGPT to engage in a conversation with itself, simulating both user and AI responses.

Copied to clipboard

Nan Xu, Fei Wang, Ben Zhou, Bangzheng Li, Chaowei Xiao, Muhao Chen

Robustness in NLP

Challenge:	Large language models (LLMs) have demonstrated increasing power, but they also have vulnerabilities.
Approach:	They propose a black-box attack that targets the cognitive structure and processes of large language models (LLMs) they propose defending cognitive overload attacks from three perspectives.
Outcome:	The proposed attack is a black-box attack with no need for knowledge of model architecture or access to model weights.

Asking Clarification Questions in Knowledge-Based Question Answering (D19-1)

Copied to clipboard

Jingjing Xu, Yuechen Wang, Duyu Tang, Nan Duan, Pengcheng Yang, Qi Zeng, Ming Zhou, Xu Sun

Natural Language Interfaces

Challenge:	Existing clarification datasets with limited annotated examples do not address ambiguous phenomena.
Approach:	They propose a dataset that allows users to ask clarification questions using open-domain examples.
Outcome:	The proposed model achieves better performance than strong baselines and provides new challenges.

OpenT2T: An Open-Source Toolkit for Table-to-Text Generation (2024.emnlp-demo)

Copied to clipboard

Haowei Zhang, Shengyun Si, Yilun Zhao, Lujing Xie, Zhijian Xu, Lyuhao Chen, Linyong Nan, Pengcheng Wang, Xiangru Tang, Arman Cohan

Can Multiple Responses from an LLM Reveal the Sources of Its Uncertainty? (2025.findings-emnlp)

Challenge:	Existing methods for table-to-text generation are limited and benchmarked on a limited number of datasets.
Approach:	They propose to use open-source tools to reproduce existing large language models for performance comparison and expedite the development of new models.
Outcome:	The proposed toolkit compares existing large language models on 9 table-to-text generation datasets and maintains a leaderboard to provide insights for future work.

Copied to clipboard

Yang Nan, Pengfei He, Ravi Tandon, Han Xu

Challenge:	Large language models can produce unreliable or misleading outputs, posing challenges for real-world applications.
Approach:	They employ an auxiliary LLM to analyze the patterns of disagreement among LLMs . they validate their framework on AmbigQA, OpenBookQA, and MMLU-Pro .
Outcome:	The proposed model can be used to diagnose uncertainty sources in a model with an auxiliary model.

ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning (2023.acl-long)

Copied to clipboard

Xiao Xu, Bei Li, Chenfei Wu, Shao-Yen Tseng, Anahita Bhiwandiwalla, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan

From Introspection to Best Practices: Principled Analysis of Demonstrations in Multimodal In-Context Learning (2025.naacl-long)

Challenge:	Two-Tower Vision-Language models suffer from ineffective layer-by-layer utilization of uni-modal representations and cannot flexibly exploit different levels of unil-modal knowledge.
Approach:	They propose a model architecture that gathers and combines the insights of pre-trained uni-modal experts at different levels to facilitate more comprehensive cross-modal alignment and fusion.
Outcome:	The proposed model outperforms baselines with and without Vision-Language Pre-training (VLP) with 4M VLP data.

Copied to clipboard

Nan Xu, Fei Wang, Sheng Zhang, Hoifung Poon, Muhao Chen

Low-Resource NLP

Challenge:	Motivated by in-context learning capabilities of Large Language Models (LLMs), multimodal LLMs with additional visual modality are also exhibited with similar ICL abilities when multiple image-text pairs are provided as demonstrations.
Approach:	They conduct systematic and principled evaluation of multimodal ICL for models of different scales on a broad spectrum of new yet critical tasks.
Outcome:	The proposed model performance improves on a broad spectrum of new yet critical tasks.

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

Copied to clipboard

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

Natural Language Interfaces

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.

MetaScale: Test-Time Scaling with Evolving Meta-Thoughts (2026.findings-acl)

Copied to clipboard

Qin Liu, Wenxuan Zhou, Nan Xu, James Y. Huang, Fei Wang, Sheng Zhang, Hoifung Poon, Muhao Chen

LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection (2026.acl-long)

Challenge:	Existing approaches impose fixed cognitive structures that enhance performance in specific tasks but lack adaptability across diverse scenarios.
Approach:	They propose a test-time scaling framework based on meta-thoughts to improve performance . meta-thinkts are adaptive thinking strategies tailored to a given task .
Outcome:	Experimental results show that MetaScale outperforms standard inference approaches . it can scale more effectively with increasing sampling budgets and produces more structured responses .

Copied to clipboard

Cheng Xu, Changhong Jin, Yingjie Niu, Nan Yan, Yuke Mei, Shuhao Guan, Liming Chen, Tahar Kechadi

Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory (2025.acl-long)

Challenge:	Current evaluation frameworks are static and vulnerable to benchmark data contamination . current models are ineffective at assessing reasoning under temporal uncertainty .
Approach:	They propose a live-based benchmark that simulates the real-world "fog of war" they propose evaluating models on their ability to reason with evolving, incomplete information .
Outcome:	The proposed model outperforms proprietary state-of-the-art models in classification and evidence mode . it also provides a component to monitor BDC explicitly .

Copied to clipboard

Yexiang Liu, Zekun Li, Zhi Fang, Nan Xu, Ran He, Tieniu Tan

Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition (2025.coling-main)

Challenge:	Recent studies have shown that scaling test-time compute can also effectively improve reasoning.
Approach:	They propose a probabilistic method to efficiently predict scaling performance and identify the best prompting strategy under large sampling times.
Outcome:	The proposed method significantly improves the scaling performance of majority voting on large language models.

Copied to clipboard

Yuming Yang, Wantong Zhao, Caishuang Huang, Junjie Ye, Xiao Wang, Huiyuan Zheng, Yang Nan, Yuran Wang, Xueying Xu, Kaixin Huang, Yunke Zhang, Tao Gui, Qi Zhang, Xuanjing Huang

Information Extraction & Text Mining

Challenge:	Current Large Language Models struggle with complex entity taxonomies in open domains and lack NER capabilities.
Approach:	They propose a dataset to guide LLMs' generalization in Open NER under a universal entity taxonomy.
Outcome:	The proposed model outperforms GPT-4 in 3 out-of-domain benchmarks across 15 datasets and 6 languages.

Evaluating Large Language Models on Controlled Generation Tasks (2023.emnlp-main)

Copied to clipboard

Jiao Sun, Yufei Tian, Wangchunshu Zhou, Nan Xu, Qian Hu, Rahul Gupta, John Wieting, Nanyun Peng, Xuezhe Ma

PromISe: Releasing the Capabilities of LLMs with Prompt Introspective Search (2024.lrec-main)

Challenge:	Recent studies have looked into the ability of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc. However, few studies investigate the controllability of large languages.
Approach:	They propose to compare large language models with state-of-the-start finetuned smaller models to find that large language model controls are comparable to smaller models.
Outcome:	The proposed model can meet hard constraints and perform better than state-of-the-art models.

Copied to clipboard

Minzheng Wang, Nan Xu, Jiahao Zhao, Yin Luo, Wenji Mao

On Diversified Preferences of Large Language Model Alignment (2024.findings-emnlp)

Challenge:	Existing evaluation benchmarks for large language models use uniform manual prompts, resulting in underestimation of performance.
Approach:	They propose a prompt introspective search framework that integrates self-introspect and self-refine to unlock the capabilities of LLMs.
Outcome:	The proposed framework significantly boosts the performance of 12 well-known LLMs compared to baseline methods.

Copied to clipboard

Dun Zeng, Yong Dai, Pengyu Cheng, Longyue Wang, Tianhao Hu, Wanshun Chen, Nan Du, Zenglin Xu

Reasoning Over Semantic-Level Graph for Fact Checking (2020.acl-main)

Challenge:	Large language models (LLMs) can be fine tuned with human feedback, but human preferences can be diversified due to annotators’ different tastes, which hinders the effectiveness of LLM alignment methods.
Approach:	They propose a calibration error metric to evaluate large language models (LLMs) and a multi-objective reward learning method to enhance the calibration performance of RMs on shared preferences.
Outcome:	The proposed model can be adopted as a key calibration error and MORE can achieve superior alignment performance.

Copied to clipboard

Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

SSA: Semantic Contamination of LLM-Driven Fake News Detection (2025.emnlp-main)

Challenge:	Existing methods for fact checking use string concatenation or fusing features of isolated evidence sentences.
Approach:	They propose a method suitable for reasoning about the semantic-level structure of evidence . they use graph convolutional network and graph attention network to exploit the structure .
Outcome:	The proposed method improves claim verification accuracy and FEVER score on a benchmark dataset.

Copied to clipboard

Cheng Xu, Nan Yan, Shuhao Guan, Yuke Mei, Tahar Kechadi

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

Challenge:	Evaluating 45 variants of nine LLMs, we find LIAR2 accuracy climbs monotonically with injected contamination, while the SSA Factor escalates in near-perfect lock-step.
Approach:	They propose a framework that detects BDC risks across semantic to label level via entity shift perturbation and an interpretable metric, the SSA Factor.
Outcome:	The proposed framework detects BDC risks across semantic to label level via entity shift perturbation and interpretable metric, the SSA Factor.

Copied to clipboard

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

TripleFact: Defending Data Contamination in the Evaluation of LLM-driven Fake News Detection (2025.acl-long)

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.

Copied to clipboard

Cheng Xu, Nan Yan

MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains (2025.findings-naacl)

Challenge:	Existing evaluation paradigms for fake news detection are based on static datasets and closed-world assumptions that are inadvertently memorized during pre-training.
Approach:	They propose a framework to mitigate BDC risk while prioritizing real-world applicability by integrating three components to assess robustness against human-crafted misinformation.
Outcome:	The proposed framework mitigates BDC risk while prioritizing real-world applicability.

Copied to clipboard

Guoli Yin, Haoping Bai, Shuang Ma, Feng Nan, Yanchao Sun, Zhaoyang Xu, Shen Ma, Jiarui Lu, Xiang Kong, Aonan Zhang, Dian Ang Yap, Yizhe Zhang, Karsten Ahnert, Vik Kamath, Mathias Berglund, Dominic Walsh, Tobias Gindele, Juergen Wiest, Zhengfeng Lai, Xiaoming Simon Wang, Jiulong Shan, Meng Cao, Ruoming Pang, Zirui Wang

Challenge:	Existing benchmarks focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes.
Approach:	They propose a Massive Multitask Agent Understanding benchmark that evaluates LLMs across five domains and offline tasks.
Outcome:	The Massive Multitask Agent Understanding (MMAU) benchmark evaluates models across five domains including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics.

Harder Task Needs More Experts: Dynamic Routing in MoE Models (2024.acl-long)

Copied to clipboard

Quzhe Huang, Zhenwei An, Nan Zhuang, Mingxu Tao, Chen Zhang, Yang Jin, Kun Xu, Kun Xu, Liwei Chen, Songfang Huang, Yansong Feng

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

Challenge:	Unlike existing MoE approaches that rely on fixed TopK Routing, our dynamic expert selection framework dynamically allocates experts based on the confidence level in expert selection for each input.
Approach:	They propose a dynamic expert selection framework that dynamically allocates experts based on the confidence level in expert selection for each input.
Outcome:	The proposed method achieves an average improvement of 0.7% with less than 90% activated parameters and outperforms dense models in QA and machine translation tasks.

Copied to clipboard

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

Multimodality

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.

mDPO: Conditional Preference Optimization for Multimodal Large Language Models (2024.emnlp-main)

Copied to clipboard

Fei Wang, Wenxuan Zhou, James Y. Huang, Nan Xu, Sheng Zhang, Hoifung Poon, Muhao Chen

Multimodality

Challenge:	Recent studies have attempted to apply DPO to multimodal scenarios but have found it challenging to achieve consistent improvement.
Approach:	They propose a multimodal DPO objective that prevents the over-prioritization of language-only preferences by also optimizing image preference.
Outcome:	The proposed method significantly improves performance on two multimodal LLMs of different sizes and three widely used benchmarks.

Monotonic Paraphrasing Improves Generalization of Language Model Prompting (2024.findings-emnlp)

Copied to clipboard

Qin Liu, Fei Wang, Nan Xu, Tianyi Yan, Tao Meng, Muhao Chen

A Contrastive Framework for Learning Sentence Representations from Pairwise and Triple-wise Perspective in Angular Space (2022.acl-long)

Challenge:	Large language models (LLMs) have demonstrated remarkable proficiency in zero-shot decision making and instruction following.
Approach:	They propose an end-to-end decoding strategy that paraphrases given prompts or instructions into their lower perplexity counterparts based on an ensemble of a paraphrase LM for prompt rewriting, and a target LM that constrains the generation for lower perxity.
Outcome:	The proposed method can efficiently paraphrase the original prompt without altering its semantic meaning while decreasing the perplexity of each generation as calculated by the target LM.

Copied to clipboard

Yuhao Zhang, Hongji Zhu, Yongliang Wang, Nan Xu, Xiaobo Li, Binqiang Zhao

A2ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization (2025.findings-acl)

Challenge:	Existing methods for learning sentence representations focus on constitution of positive and negative representation pairs and do not focus on training objective.
Approach:	They propose a new method to learn sentence representations using BERT-like pre-trained models . they use a pairwise discriminating power and a model to model the entailment relation of triplet sentences .
Outcome:	The proposed method outperforms the previous state-of-the-art on diverse sentence related tasks.

Copied to clipboard

Junhui He, Junna Xing, Nan Wang, Rui Xu, Shangyu Wu, Peng Zhou, Qiang Liu, Chun Jason Xue, Qingan Li

KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering (2025.findings-emnlp)

Challenge:	Long context large language models (LLMs) pose significant challenges for efficient serving due to the large memory footprint and high access overhead of KV cache.
Approach:	They propose a retrieval-based method to reduce the memory footprint of LLMs . they propose Windowed Rotary Position Embedding and query-aware vector quantization .
Outcome:	The proposed method can achieve lower performance degradation with lower overhead compared to existing methods . it can reduce the memory footprint and access overhead of long context large language models .

Copied to clipboard

Yushi Sun, Kai Sun, Yifan Ethan Xu, Xiao Yang, Xin Luna Dong, Nan Tang, Lei Chen

Structured Data in NLP

Challenge:	Traditional Knowledge Graph Question Answering (KGQA) methods rely on semantic parsing to retrieve knowledge strictly necessary for answer generation.
Approach:	They propose a retrieval-filtering-summarization pipeline that enhances QA coverage by retrieving a broader subgraph likely to contain relevant information.
Outcome:	The proposed pipeline surpasses state-of-the-art solutions by about 7% in quality and exceeds GPT-4o (Tool) by 10-21%.

Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)

Copied to clipboard

Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li

A Theory Guided Scaffolding Instruction Framework for LLM-Enabled Metaphor Reasoning (2024.naacl-long)

Challenge:	Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications.
Approach:	They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) .
Outcome:	The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents.

Copied to clipboard

Yuan Tian, Nan Xu, Wenji Mao

Linguistics & Cognitive NLP

Challenge:	Existing methods for metaphor detection and reasoning struggle to explain the underlying reasoning process behind the metaphorical/literal judgment.
Approach:	They propose a Theory guided Scaffolding Instruction framework that instructs an LLM to infer the underlying reasoning process of metaphor detection guided by metaphor theories for the first time.
Outcome:	The proposed method significantly outperforms both the LLM-based reasoning methods and the SOTA methods in metaphor detection.

Syntax-Enhanced Pre-trained Model (2021.acl-long)

Copied to clipboard

Zenan Xu, Daya Guo, Duyu Tang, Qinliang Su, Linjun Shou, Ming Gong, Wanjun Zhong, Xiaojun Quan, Daxin Jiang, Nan Duan

Syntactic Text Processing

Challenge:	Existing methods that use syntax of text in pre-training and fine-tuning suffer from discrepancy between the two stages.
Approach:	They propose a model that utilizes the syntactic structure of text in pre-training and fine-tuning stages.
Outcome:	The proposed model achieves state-of-the-art on six public benchmark datasets.

Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing (2022.emnlp-main)

Copied to clipboard

Nan Xu, Fei Wang, Bangzheng Li, Mingtao Dong, Muhao Chen

LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics (N18-1)

Challenge:	Existing entity typing models are subject to spurious correlations due to shortcuts and biased training.
Approach:	They propose a method to augment existing model biases by combining spurious correlations with debiasedcounterparts to improve generalization.
Outcome:	The proposed method improves generalization of different entity typing models on the original and debiased test sets.

Copied to clipboard

Zhen Xu, Nan Jiang, Bingquan Liu, Wenge Rong, Bowen Wu, Baoxun Wang, Zhuoran Wang, Xiaolong Wang

Analytical Reasoning of Text (2022.findings-naacl)

Challenge:	Existing evaluation metrics for NRG models can't measure semantic relevance and diversity of generated results.
Approach:	They propose a large-scale domain-specific conversational corpus with preprocessing and cleansing procedures for model training and a testing set for measuring the diversity of generated results.
Outcome:	The proposed corpus can be taken as a new benchmark dataset for the NRG task.

Copied to clipboard

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

Neural Deepfake Detection with Factual Structure of Text (2020.emnlp-main)

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.

Copied to clipboard

Wanjun Zhong, Duyu Tang, Zenan Xu, Ruize Wang, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

Structured Data in NLP

Challenge:	Existing approaches to deepfake detection typically represent documents with coarse-grained representations, but they struggle to capture factual structures of documents.
Approach:	They propose a graph-based model that captures factual structures of documents for deepfake detection.
Outcome:	The proposed model improves strong base models built with RoBERTa on two public deepfake datasets.

Dense Retrieval as Indirect Supervision for Large-space Decision Making (2023.findings-emnlp)

Copied to clipboard

Nan Xu, Fei Wang, Mingtao Dong, Muhao Chen

Information Retrieval

Challenge:	Dense Decision Retrieval (DDR) is a learning-to-retrieve task for discriminative natural language understanding (NLU) tasks with large label spaces.
Approach:	They propose a novel approach to learning large-space discriminative NLU tasks as a learning-to-retrieve task by adopting a dual-encoder architecture that learns to predict by retrieving from a decision thesaurus.
Outcome:	The proposed approach outperforms baselines greatly on multi-label classification tasks, 1.17% in F1 score ultra-fine entity typing, and 1.26% in accuracy on three few-shot intent classification tasks on average.

ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering (2025.emnlp-main)

Copied to clipboard

Jingxuan Wei, Nan Xu, Junnan Zhu, null Haoyanni, Gaowei Wu, Qi Chen, Bihui Yu, Lei Wang

Natural Language Interfaces

Challenge:	Chart question answering (CQA) is a multimodal task for evaluating the reasoning capabilities of vision-language models.
Approach:	They propose a chart question answering benchmark that incorporates multilingual contexts and supports open-domain textual outputs.
Outcome:	The proposed framework outperforms the previous three common CQA paradigms: instruction-following, OCR-enhanced, and chain-of-thought.

Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld’s Episode Theory (2025.emnlp-main)

Copied to clipboard

Ming Li, Nan Zhang, Chenrui Fan, Hong Jiao, Yanbin Fu, Sydney Peters, Qingshu Xu, Robert Lissitz, Tianyi Zhou

Linguistics & Cognitive NLP

Challenge:	Large Reasoning Models (LRMs) generate extensive chain-of-thought reasoning, but we lack a principled framework for understanding how these thoughts are structured.
Approach:	They propose a method to analyze the reasoning traces of Large Reasoning Models using Schoenfeld’s Episode Theory.
Outcome:	The proposed framework provides a theoretically grounded methodology for interpreting LRM cognition and enables future work on more controllable and transparent reasoning systems.

DCR: Quantifying Data Contamination in LLMs Evaluation (2025.emnlp-main)

Copied to clipboard

Cheng Xu, Nan Yan, Shuhao Guan, Changhong Jin, Yuke Mei, Yibing Guo, Tahar Kechadi

Challenge:	Large language models (LLMs) memorize evaluation data during training, inflating performance metrics and undermining genuine generalization assessment.
Approach:	They propose a framework to detect and quantify benchmark data contamination (BDC) by synthesizing contamination scores via a fuzzy inference system.
Outcome:	The proposed framework detects and quantifies BDC risk across semantic, informational, data, and label levels.

GPS: Genetic Prompt Search for Efficient Few-Shot Learning (2022.emnlp-main)

Copied to clipboard

Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Wang Yanggang, Haiyu Li, Zhilin Yang

Target-Oriented Relation Alignment for Cross-Lingual Stance Detection (2023.findings-acl)

Challenge:	Pretrained language models are often finetuned for downstream tasks, which has been shown to improve performance over non-pretrained models.
Approach:	They propose a genetic algorithm to automatically search for the best prompt for few-shot learning with pretrained language models by gradient-free algorithm.
Outcome:	Experiments on diverse datasets show that the proposed method outperforms manual prompts by 2.6 points.

Copied to clipboard

Ruike Zhang, Nan Xu, Hanxuan Yang, Yuan Tian, Wenji Mao

Bridging Word-Pair and Token-Level Metaphor Detection with Explainable Domain Mining (2024.acl-long)

Challenge:	Existing work on cross-lingual stance detection has ignored the inconsistency in the occurrences and distributions of targets between languages, which consequently degrades the performance of stance detector in low-resource languages.
Approach:	They propose a fine-grained method which considers both target-level associations and language-level alignments to learn the in-language and cross-language associations.
Outcome:	The proposed method is compared with competing methods under variant settings and shows that it performs better in low-resource languages.

Copied to clipboard

Yuan Tian, Ruike Zhang, Nan Xu, Wenji Mao

Explainability & Interpretability in NLP

Challenge:	Existing methods for word-pair metaphor detection provide intermediate explainable clues for detection results.
Approach:	They propose a method to bridge word-pair and token-level metaphor detection by modeling word pairs as explainable intermediate information.
Outcome:	The proposed method bridges word-pair and token-level metaphor detection by using word pairs . it provides intermediate explainable clues for the detection results, but this is a challenge .

TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answering (2025.emnlp-main)

Copied to clipboard

Junnan Zhu, Jingyi Wang, Bohan Yu, Xiaoyu Wu, Junbo Li, Lei Wang, Nan Xu

Structured Data in NLP

Challenge:	Existing TableQA benchmarks focus on simple flat tables and suffer from data leakage . current benchmarks are monolingual and fail to capture cross-lingual variability .
Approach:	They propose a table-based TableQA benchmark to evaluate LLMs on real-world tasks.
Outcome:	The proposed benchmarks show that they achieve high agreement with human judgment . the proposed framework improves on the alignment between model responses and reference answers .

LLM The Genius Paradox: A Linguistic and Math Expert’s Struggle with Simple Word-based Counting Problems (2025.naacl-long)

Copied to clipboard

Nan Xu, Xuezhe Ma

CEO: Corpus-based Open-Domain Event Ontology Induction (2024.findings-eacl)

Challenge:	Existing conjectures about the reason for deficiency of LLMs in simple word-based counting problems are invalid.
Approach:	They propose to evaluate model transferability from specialized LLMs to simple counting tasks by comparing their results to popular conjectures .
Outcome:	The proposed model evaluations show that engaging reasoning is the most robust and efficient way to help LLMs better perceive tasks with more accurate responses.

Copied to clipboard

Nan Xu, Hongming Zhang, Jianshu Chen

Knowledge Representation

Challenge:	Existing event-centric NLP models restrict their generalization capabilities by limiting the pre-defined ontology.
Approach:	They propose a Corpus-based Event Ontology induction model to relax the restriction imposed by pre-defined ontologies.
Outcome:	The proposed model can induce a hierarchical event ontology with meaningful names on eleven open-domain corpora, making it more trustworthy and easier to be further curated.

QTSumm: Query-Focused Summarization over Tabular Data (2023.emnlp-main)

Copied to clipboard

Yilun Zhao, Zhenting Qi, Linyong Nan, Boyu Mi, Yixin Liu, Weijin Zou, Simeng Han, Ruizhe Chen, Xiangru Tang, Yumo Xu, Dragomir Radev, Arman Cohan

Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity (D19-1)

Challenge:	Existing text generation systems that can provide accurate table summaries can facilitate more efficient access to relevant data insights.
Approach:	They propose a query-focused task where text generation models have to perform human-like reasoning and analysis over the given table to generate a tailored table summary.
Outcome:	The proposed method improves existing baselines on table-to-text generation and large language models by concatenating generated facts to the model input.

Copied to clipboard

Penghui Wei, Nan Xu, Wenji Mao

Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association (2020.acl-main)

Challenge:	Existing methods to verify rumors are needed to identify false rumors.
Approach:	They propose a hierarchical multi-task learning framework for jointly predicting rumor stance and veracity on Twitter that exploits the temporal dynamics of stance evolution.
Outcome:	The proposed framework outperforms previous methods on two benchmark datasets showing that it can predict rumor stance and veracity.

Copied to clipboard

Nan Xu, Zhixiong Zeng, Wenji Mao

Sentiment Analysis

Challenge:	Existing methods for sarcasm detection rely on text data, but are insufficient to detect multimodal sarcasm.
Approach:	They propose a method for modeling cross-modality contrast in the associated context by constructing the Decomposition and Relation Network.
Outcome:	The proposed model can detect sarcasm in multimodal tweets using a dataset .

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

Copied to clipboard

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

Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents (2026.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.

Copied to clipboard

Minzheng Wang, Run Luo, Yanbo Wang, Zichen Liu, Yuqiao Tan, Tao Tan, Nan Xu, Lu Wang, Wenji Mao

ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-Shot Generalization (2022.findings-emnlp)

Challenge:	Reinforcement Learning with Verifiable Rewards (RLVR) is effective for closed-ended tasks, but it is not applicable to open-ended social language games.
Approach:	They propose a method that uses a time-scaled evolutionary perception mechanism to detect impasse by quantifying dual-scale value baseline divergence alongside match entropy.
Outcome:	Experiments on multiple social language games show that the proposed method outperforms baselines and avoids policy degeneration.

Copied to clipboard

Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Wang Yanggang, Haiyu Li, Zhilin Yang

Rhombus: Incentivizing Coordination in Parallel Thinking through Reinforcement Learning (2026.findings-acl)

Challenge:	a recent study shows that task scaling can be an efficient alternative to model scaling.
Approach:	They propose a multitask pretraining approach ZeroPrompt for zero-shot generalization . they focus on task scaling and zero-shooting to improve model performance .
Outcome:	The proposed approach improves zero-shot generalization efficiency by 30 times with task scaling.

Copied to clipboard

Ziyuan Nan, Qi Yi, Di Huang, Yutong Wu, Guanhua Huang, Xue Gong, Kejiao Li, Yuhao Jiang, Chenchen Zhang, Zenan Xu, Xing Hu, Bo Zhou