Papers by Nan Yang

Attention-Guided Answer Distillation for Machine Reading Comprehension (D18-1)

Challenge:	Existing studies on cognitive distortion have limited generalizability and performance of models in large-scale and cross-linguistic contexts.
Approach:	They propose a multi-task learning model based on teacher student architecture solution which improves generalization performance.
Outcome:	The proposed model improves generalizability and interpretability of the proposed model.

Copied to clipboard

Minghao Hu, Yuxing Peng, Furu Wei, Zhen Huang, Dongsheng Li, Nan Yang, Ming Zhou

AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents (2025.findings-acl)

Challenge:	Existing approaches to reading comprehension systems are vulnerable to adversarial attacks.
Approach:	They propose to use knowledge distillation to transfer knowledge from an ensemble to a single model.
Outcome:	The proposed methods outperform the teacher on adversarial datasets and NarrativeQA benchmarks.

Copied to clipboard

Guhong Chen, Liyang Fan, Zihan Gong, Nan Xie, Zixuan Li, Ziqiang Liu, Chengming Li, Qiang Qu, Hamid Alinejad-Rokny, Shiwen Ni, Min Yang

Robustness in NLP

Challenge:	Existing legal language models struggle with dynamic courtroom interactions, resulting in overfitting to standardized legal tasks.
Approach:	They propose a new adversarial evolutionary approach for agents that performs dynamic knowledge learning and evolution through structured adversarials in a simulated courtroom program.
Outcome:	The proposed approach outperforms existing LLM-based models in three critical dimensions: cognitive agility, professional knowledge, and logical rigor.

Benchmarking Data Science Agents (2024.acl-long)

Copied to clipboard

Yuge Zhang, Qiyang Jiang, XingyuHan XingyuHan, Nan Chen, Yuqing Yang, Kan Ren

Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) have emerged as promising data science aids, assisting humans in data analysis and processing.
Approach:	They propose an evaluation paradigm and benchmarks that assess the performance of data science agents throughout the entire data science lifecycle.
Outcome:	The proposed evaluation paradigm streamlines dataset preparation, improves coverage, and expands benchmarking comprehensiveness.

Copied to clipboard

Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou

Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models (2024.findings-naacl)

Challenge:	Existing EE datasets define fixed event types and design specific schemas for each of them, failing to cover diverse events emerging from the online text.
Approach:	They propose to use a sentence-level dataset to benchmark Open Event Extraction without restricting event types.
Outcome:	The proposed dataset contains more than 42,000 news titles in 34 topics collected from Chinese web pages.

Copied to clipboard

Fobo Shi, Peijun Qing, Dong Yang, Nan Wang, Youbo Lei, Haonan Lu, Xiaodong Lin, Duantengchuan Li

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

Challenge:	Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions.
Approach:	They propose a new approach that uses text embeddings to obtain basis vectors by matrix decomposition and constructs a space for representing all prompts.
Outcome:	The proposed approach significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks.

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.

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

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.

SwiLTra-Bench: The Swiss Legal Translation Benchmark (2025.acl-long)

Copied to clipboard

Joel Niklaus, Jakob Merane, Luka Nenadic, Sina Ahmadi, Yingqiang Gao, Cyrill A. H. Chevalley, Claude Humbel, Christophe Gösken, Lorenzo Tanzi, Thomas Lüthi, Stefan Palombo, Spencer Poff, Boling Yang, Nan Wu, Matthew Guillod, Robin Mamié, Daniel Brunner, Julio Pereyra, Niko Grupen

Multilinguality

Challenge:	In Switzerland legal translation relies on legal experts who must be both legal experts and skilled translators—creating bottlenecks and impacting effective access to justice.
Approach:	They propose a multilingual benchmarking system that evaluates Swiss legal translation systems based on 180K aligned Swiss legal translator pairs . they show frontier models achieve superior translation performance across all document types while specialized translation systems excel specifically in laws but under-perform in headnotes.
Outcome:	The proposed model outperforms specialized models in laws but underperform in headnotes.

Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension (D19-58)

Copied to clipboard

Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Lei Cui, Songhao Piao, Ming Zhou

MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media (2025.emnlp-main)

Challenge:	Experimental results show that unified model outperforms other models that treat encoding and matching separately.
Approach:	They evaluate a unified model with Transformer layers for machine reading comprehension . they find that the model learns different modeling strategies compared with previous models .
Outcome:	The unified model outperforms models with Transformer layers on the machine reading comprehension task.

Copied to clipboard

Wei Zhai, Nan Bai, Qing Zhao, Jianqiang Li, Fan Wang, Hongzhi Qi, Meng Jiang, Xiaoqin Wang, Bing Xiang Yang, Guanghui Fu

Examining False Positives under Inference Scaling for Mathematical Reasoning (2025.emnlp-main)

Challenge:	Social media is a key platform for emotional expression, yet deep learning lacks flexibility and interpretability.
Approach:	They propose to use Chinese social media to train interpretable mental health instruction datasets to test models' ability to explain their decisions.
Outcome:	The proposed models outperform deep learning and LLMs on three mental health downstream tasks and demonstrate their potential for clinical applications.

Copied to clipboard

Yu Wang, Nan Yang, Liang Wang, Furu Wei, Fuli Feng

Reasoning

Challenge:	Recent advances in language models have led to significant improvements in mathematical reasoning across benchmarks.
Approach:	They analyze the prevalence of false positives in language models by using heuristic evaluation methods . they find that false positive models produce correct final answers but with flawed deduction paths .
Outcome:	The proposed model performance improvements are based on the proposed model and its evaluation metrics.

Adversarial Preference Optimization: Enhancing Your Alignment via RM-LLM Game (2024.findings-acl)

Copied to clipboard

Pengyu Cheng, Yifan Yang, Jian Li, Yong Dai, Tianhao Hu, Peixin Cao, Nan Du, Xiaolong Li

Robustness in NLP

Challenge:	Existing methods for training large language models require additional annotations to adjust to shifted distributions.
Approach:	They propose an algorithm that allows LLMs and reward models to update alternatively via a min-max game to improve their alignment.
Outcome:	The proposed framework improves existing alignment baselines in terms of LLM helpfulness and harmlessness.

Universal Sentence Encoder for English (D18-2)

Copied to clipboard

Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Brian Strope, Ray Kurzweil

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

Challenge:	TensorFlow Hub sentence embedding models have good task transfer performance . model variants allow for trade-offs between accuracy and compute resources .
Approach:	They propose easy-to-use TensorFlow Hub sentence embedding models with good task transfer performance.
Outcome:	The proposed models outperform models without transfer learning and those that use only word-level transfer on a number of NLP tasks.

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

Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metric (2025.acl-long)

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.

Copied to clipboard

Yuming Yang, Yang Nan, Junjie Ye, Shihan Dou, Xiao Wang, Shuo Li, Huijie Lv, Tao Gui, Qi Zhang, Xuanjing Huang

CMMLU: Measuring massive multitask language understanding in Chinese (2024.findings-acl)

Challenge:	Existing studies have explored various diversity-aware data selection methods to construct high-quality datasets and enhance model performance.
Approach:	They propose to use data diversity to measure instruction tuning of large language models.
Outcome:	The proposed diversity metric outperforms existing methods on simulated and real-world data and shows that it captures diversity variations and achieves a 0.97 correlation with instruction tuning.

Copied to clipboard

Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin

xMoCo: Cross Momentum Contrastive Learning for Open-Domain Question Answering (2021.acl-long)

Challenge:	Existing large language models struggle to achieve an accuracy of even 60%, which is the pass mark for Chinese exams.
Approach:	They propose to use CMMLU to evaluate Chinese multilingual and Chinese LLMs in a comprehensive benchmark that covers various subjects and settings.
Outcome:	The proposed benchmark covers natural sciences, social sciences, engineering, and the humanities and aims to improve on existing models.

Copied to clipboard

Nan Yang, Furu Wei, Binxing Jiao, Daxing Jiang, Linjun Yang

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

Challenge:	Existing approaches to find relevant passages using sparse keywords are not effective for open domain question answering.
Approach:	They propose a new contrastive learning method for learning a dual-encoder model for question-passage matching using a large pool of negative samples.
Outcome:	The proposed method maintains large pool of negative samples and optimizes question-to-passage and passage-to question matching tasks.

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

Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior (2020.findings-emnlp)

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

Zi Lin, Jeremiah Liu, Zi Yang, Nan Hua, Dan Roth

DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)

Challenge:	Spectral-normalized identity priors (SNIP) is a structured pruning approach for a Transformer model.
Approach:	They propose a structured pruning approach which penalizes an entire residual module toward an identity mapping.
Outcome:	The proposed method improves on 5 GLUE benchmark tasks while maintaining comparable performance.

Copied to clipboard

Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang

Challenge:	Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability.
Approach:	They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence.
Outcome:	The proposed model outperforms baselines on three real-world datasets.

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

Copied to clipboard

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.

Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge (C18-1)

Copied to clipboard

Yang Deng, Ying Shen, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei

Question Answering

Challenge:	Existing approaches to answer selection are limited in domains with limited labeled data.
Approach:	They propose a Knowledge-aware Attentive Network framework for cross-domain answer selection that uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domain.
Outcome:	The proposed model outperforms strong competitors by a noticeable margin in cross-domain answer selection.

Streaming Hallucination Detection in Long Chain-of-Thought Reasoning (2026.findings-acl)

Copied to clipboard

Haolang Lu, Minghui Pan, Ripeng LI, Guoshun Nan, Jialin Zhuang, Zijie Zhao, Zhongxiang Sun, Kun Wang, Yang Liu

Reasoning

Challenge:	Long chain-of-thought reasoning improves performance of large language models, yet hallucinations in such settings often emerge subtly and propagate across reasoning steps.
Approach:	They propose to treat step-level hallucination judgments as local observations and introduce a cumulative prefix-level signal that tracks the global evolution of the reasoning state over the entire trajectory.
Outcome:	The proposed method enables streaming hallucination detection in long CoT reasoning, providing real-time, interpretable evidence.

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

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

Cooperative Denoising for Distantly Supervised Relation Extraction (C18-1)

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

Kai Lei, Daoyuan Chen, Yaliang Li, Nan Du, Min Yang, Wei Fan, Ying Shen

MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)

Challenge:	Existing methods for distantly supervised relation extraction suffer from noisy labeling problem, which can severely degrade its performance.
Approach:	They propose a framework for distantly supervised relation extraction that leverages text corpus and knowledge graph and a cooperative module involving their mutual learning.
Outcome:	The proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods.

Copied to clipboard

Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li

Multilinguality

Challenge:	Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities.
Approach:	They propose a comprehensive benchmark covering 29 languages, built on an English benchmark.
Outcome:	The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark.

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

Copied to clipboard

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.

Little Giants: Synthesizing High-Quality Embedding Data at Scale (2025.naacl-long)

Copied to clipboard

Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, Zhicheng Dou

SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training (2024.acl-long)

Challenge:	Synthetic data generation is an increasingly popular way of training models without the need for large, manually labeled datasets.
Approach:	They propose a framework that aligns open-source small models to efficiently generate large-scale embedding data.
Outcome:	The proposed framework outperforms state-of-the-art embedding models by using only 1/10 of the GPT API calls.

Copied to clipboard

Nan He, Weichen Xiong, Hanwen Liu, Yi Liao, Lei Ding, Kai Zhang, Guohua Tang, Xiao Han, Yang Wei

Improving Text Embeddings with Large Language Models (2024.acl-long)

Challenge:	Current methods focus on detecting and removing duplicates, which risks the loss of valuable information and neglects the varying degrees of duplication.
Approach:	They propose a method that maintains dataset integrity while selectively reducing the sampling weight of data with high commonness.
Outcome:	The proposed method significantly improves training efficiency on deduplicated datasets and improves downstream accuracy by 1.77%.

Copied to clipboard

Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei

Hierarchical Reinforcement Learning with Augmented Step-Level Transitions for LLM Agents (2026.acl-long)

Challenge:	Existing methods for obtaining text embeddings require complex training pipelines . authors leverage proprietary LLMs to generate diverse synthetic data for text embeds based on 93 languages .
Approach:	They propose a method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps.
Outcome:	The proposed method achieves strong performance on competitive text embedding benchmarks without using any labeled data.

Copied to clipboard

Shuai Zhen, Yanhua Yu, Ruopei Guo, Nan Cheng, Yang Deng

Challenge:	Existing Large language model agents rely on increasingly long interaction histories, resulting in high computational cost and limited scalability.
Approach:	They propose a hierarchical reinforcement learning framework that enables step-level learning by conditioning only on single-step transitions rather than full interaction histories.
Outcome:	The proposed framework outperforms baselines on ScienceWorld and ALFWorld benchmarks in terms of performance and generalization while reducing token usage.

SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval (2023.acl-long)

Copied to clipboard

Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei

Noise Robust Named Entity Understanding for Voice Assistants (2021.naacl-industry)

Challenge:	SimLM uses a simple bottleneck architecture that learns to compress the passage information into a dense vector through self-supervised pre-training.
Approach:	They propose a simple yet effective pre-training method for dense passage retrieval that learns to compress the passage information into a dense vector through self-supervised pre-tuning.
Outcome:	The proposed method outperforms multi-vector approaches on large-scale passage retrieval datasets and shows significant improvements over baselines.

Copied to clipboard

Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang, Justine Kao, Stephen Pulman, Atish Kothari, Ray Shen, Yinying Pan, Vivek Kaul, Mubarak Seyed Ibrahim, Gang Xiang, Nan Dun, Yidan Zhou, Andy O, Yuan Zhang, Pooja Chitkara, Xuan Wang, Alkesh Patel, Kushal Tayal, Roger Zheng, Peter Grasch, Jason D Williams, Lin Li

Robustness in NLP

Challenge:	Named Entity Recognition and Entity Linking are challenging for voice assistants . utterances are relatively short, so there is not much context to help disambiguate .
Approach:	They propose a Named Entity Understanding system that combines NER and EL in a joint reranking module.
Outcome:	The proposed framework improves NER accuracy by up to 3.13% and EL accuracy by 3.6% in F1 score . it also leads to better accuracies in other natural language understanding tasks .

Multiview Identifiers Enhanced Generative Retrieval (2023.acl-long)

Copied to clipboard

Yongqi Li, Nan Yang, Liang Wang, Furu Wei, Wenjie Li

Information Retrieval

Challenge:	Current approaches use a numeric ID or text piece as the identifier, but these identifieres cannot cover a passage’s content well.
Approach:	They propose a new type of identifier that is generated based on the content of a passage and could integrate contextualized information that text pieces lack.
Outcome:	The proposed approach performs the best in generative retrieval on three public datasets.

Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge (2021.acl-long)

Copied to clipboard

Linmei Hu, Tianchi Yang, Luhao Zhang, Wanjun Zhong, Duyu Tang, Chuan Shi, Nan Duan, Ming Zhou

Ethical NLP

Challenge:	Existing methods for fake news detection rely on linguistic and semantic features from news content and do not exploit external knowledge.
Approach:	They propose a graph neural model which compares news to knowledge base through entities for fake news detection.
Outcome:	The proposed model significantly outperforms state-of-the-art methods on two benchmark datasets.

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

mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data (2025.findings-acl)

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

Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, Zhicheng Dou

NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation (2023.acl-long)

Challenge:	Multimodal embedding models encode multimedia inputs into latent vector representations.
Approach:	They propose to synthesize multimodal multilingual data using a multimodal large language model . they identify three criteria for high-quality synthetic multimodal data .
Outcome:	The proposed model outperforms existing models on the MMEB Benchmark and the XTD benchmark.

Copied to clipboard

Shengming Yin, Chenfei Wu, Huan Yang, Jianfeng Wang, Xiaodong Wang, Minheng Ni, Zhengyuan Yang, Linjie Li, Shuguang Liu, Fan Yang, Jianlong Fu, Ming Gong, Lijuan Wang, Zicheng Liu, Houqiang Li, Nan Duan

Neural Document Summarization by Jointly Learning to Score and Select Sentences (P18-1)

Challenge:	Existing work generates long videos segment by segment sequentially, which is inefficient.
Approach:	They propose a Diffusion over Difference architecture for eXtremely Long video generation.
Outcome:	The proposed architecture reduces the average inference time from 7.55min to 26s (94.26%) and generates high-quality long videos with both global and local coherence.

Copied to clipboard

Qingyu Zhou, Nan Yang, Furu Wei, Shaohan Huang, Ming Zhou, Tiejun Zhao

Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels (2025.emnlp-main)

Challenge:	Sentence scoring and sentence selection are two main steps in extractive document summarization systems.
Approach:	They propose an end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences.
Outcome:	The proposed framework outperforms the state-of-the-art summarization models on the CNN/Daily Mail dataset.

Copied to clipboard

Junjie Ye, Yuming Yang, Yang Nan, Shuo Li, Qi Zhang, Tao Gui, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan

SaSR-Net: Source-Aware Semantic Representation Network for Enhancing Audio-Visual Question Answering (2024.findings-emnlp)

Challenge:	Large language models (LLMs) acquire substantial world knowledge during pretraining, which is further shaped by post-training techniques such as supervised fine-tuning (SFT).
Approach:	They evaluate closed-book question answering (CBQA) performance across five LLMs from the LLaMA-2 and LLama-3 families and examine the impact of supervised fine-tuning on model knowledge.
Outcome:	The proposed model performance is 14% worse than models fine-tuned on 1,920 samples and 12% worse on 240 samples.

Copied to clipboard

Tianyu Yang, Yiyang Nan, Lisen Dai, Zhenwen Liang, Yapeng Tian, Xiangliang Zhang

LongEmbed: Extending Embedding Models for Long Context Retrieval (2024.emnlp-main)

Challenge:	Existing AVQA methods often fail to link sound-producing objects in the video with the audio-visual information.
Approach:	They introduce a source-aware semantic representation network for AVQA . they use source-wise learnable tokens to capture and align audio-visual elements with the question .
Outcome:	The proposed model outperforms state-of-the-art models on the Music-AVQA and AVQA-Yang datasets.

Copied to clipboard

Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wenhao Wu, Furu Wei, Sujian Li

Representation Learning

Challenge:	Existing embedding models support only 512 input tokens, hindering their application in scenarios requiring long inputs.
Approach:	They evaluate the performance of existing embedding models by using a new benchmark and a training-free context window extension strategy.
Outcome:	The proposed model extends the input window of existing models by several folds.

Query2doc: Query Expansion with Large Language Models (2023.emnlp-main)

Copied to clipboard

Liang Wang, Nan Yang, Furu Wei

Learning to Retrieve In-Context Examples for Large Language Models (2024.eacl-long)

Challenge:	Existing methods for sparse and dense retrieval have limited success on popular datasets.
Approach:	They propose a query expansion approach that generates pseudo-documents by few-shot prompting large language models and then expands the query with generated pseudo-docs.
Outcome:	The proposed method boosts the performance of BM25 on ad-hoc IR datasets by 3% to 15% without any model fine-tuning.

Copied to clipboard

Liang Wang, Nan Yang, Furu Wei

MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings (2026.acl-long)

Challenge:	Existing approaches to improve in-context learning performance are highly sensitive to the quality of the incontext examples provided.
Approach:	They propose a framework to iteratively train dense retrievers that can identify high-quality in-context examples for large language models.
Outcome:	The proposed model improves performance by retrieving examples with similar patterns, and the gains are consistent across LLMs of varying sizes.

Copied to clipboard

Haonan Chen, Hong Liu, Yuping Luo, Liang Wang, Nan Yang, Furu Wei, Zhicheng Dou

Allies: Prompting Large Language Model with Beam Search (2023.findings-emnlp)

Challenge:	Existing approaches to embed multimodal models face limitations such as suboptimal causal attention in VLMs and limited diversity in training objectives and data.
Approach:	They propose a framework for transforming pre-trained VLMs into bidirectional multimodal embedding models.
Outcome:	The proposed model improves performance across MMEB and ViDoRe-v2 benchmarks and exhibits strong scalability with both model size and training data on MMEF.

Copied to clipboard

Hao Sun, Xiao Liu, Yeyun Gong, Yan Zhang, Daxin Jiang, Linjun Yang, Nan Duan

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

Challenge:	Existing methods to build LLMs with stacking are limited by their information coverage and low fault tolerance.
Approach:	They propose a method that leverages large language models to iteratively generate new queries from an input query.
Outcome:	The proposed method outperforms baselines on open-domain question answering benchmarks.

Copied to clipboard

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

Multi-grained Named Entity Recognition (P19-1)

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

Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, Philip Yu

A Discourse-Aware Graph Neural Network for Emotion Recognition in Multi-Party Conversation (2021.findings-emnlp)

Challenge:	Existing approaches treat Named Entity Recognition (NER) as a sequence labeling task.
Approach:	They propose a framework for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.
Outcome:	The proposed framework outperforms current state-of-the-art frameworks by 4.4% in terms of the F1 score among nested/non-overlapping NER tasks.

Copied to clipboard

Yang Sun, Nan Yu, Guohong Fu

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

Challenge:	Prior research focuses on exploring sequential information but ignores the discourse structures of conversations.
Approach:	They propose a discourse-aware graph neural network (ERMC-DisGCN) that leverages contextual cues and speaker-specific features for ERMC.
Outcome:	The proposed method outperforms multiple baselines showing that discourse structures are of great value to ERMC.

Copied to clipboard

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

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

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

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