Papers by Bo Wu

Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network (2021.naacl-main)

Challenge:	Existing datasets often rely on synthetic data or figure-caption pairs, failing to capture the depth and complexity of geoscientific reasoning.
Approach:	They propose a multimodal scientific dataset and benchmark curated from open-access publications.
Outcome:	MSEarth features over 289K figures with captions enriched by contextual discussions and reasoning from original papers.

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Haoran Wu, Wei Chen, Shuang Xu, Bo Xu

Knowledge Representation

Challenge:	Existing methods provide explanations based on a precise medical knowledge base, which is disease-specific and difficult to obtain for experts in reality.
Approach:	They propose a method to extract supporting facts from irregular EMR without external knowledge bases by constructing a hierarchical graph network and using it to obtain causal relationship between multi-granularity features and diagnosis results.
Outcome:	The proposed method diagnoses four types of EMR correctly and provides accurate supporting facts for the results.

RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction (2022.findings-naacl)

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Shusen Wang, Bosen Zhang, Yajing Xu, Yanan Wu, Bo Xiao

SCRIPT: Self-Critic PreTraining of Transformers (2021.naacl-main)

Challenge:	Existing approaches to extract relations require large-scale labeled data.
Approach:	They propose a Relation Contrastive Learning framework to mitigate similar relations and similar entities problems by optimizing a contrastive instance loss with a relation classification loss on seen relations.
Outcome:	The proposed framework can learn subtle difference between instances and achieve better separation between different relation categories in the representation space simultaneously.

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Erik Nijkamp, Bo Pang, Ying Nian Wu, Caiming Xiong

From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework (2023.findings-acl)

Challenge:	Existing methods for representation learning of text are masked language modeling (MLM) a language model is trained to learn universal contextual embeddings, which are fine-tuned on a down-stream task.
Approach:	They propose a self-critic pretraining transformer for representation learning of text . they demonstrate improved sample-efficiency and improved performance over strong baselines .
Outcome:	The proposed model improves sample-efficiency and performance over strong baselines.

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Yangyi Chen, Hongcheng Gao, Ganqu Cui, Lifan Yuan, Dehan Kong, Hanlu Wu, Ning Shi, Bo Yuan, Longtao Huang, Hui Xue, Zhiyuan Liu, Maosong Sun, Heng Ji

Robustness in NLP

Challenge:	Existing models of robustness evaluation are incomprehensive, impractical, and invalid .
Approach:	They propose a framework for automatic robustness evaluation that shifts towards model-centric evaluation to further exploit the advantages of adversarial attacks.
Outcome:	The proposed framework is based on a model-centric evaluation protocol and a robustness evaluation protocol.

GeoLaux: A Benchmark for Evaluating MLLMs’ Geometry Performance on Long-Step Problems Requiring Auxiliary Lines (2026.acl-long)

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Yumeng Fu, Jiayin Zhu, Lingling Zhang, Wenjun Wu, Bo Zhao, Shaoxuan Ma, Yushun Zhang, Jun Liu

Reasoning

Challenge:	Existing benchmarks for Geometry problem solving lack fine-grained evaluation for long-step problems necessitating auxiliary line construction.
Approach:	They present a fine-grained annotated dataset with long-step reasoning and auxiliary line construction that provides a detailed evaluation of 23 leading MLLMs.
Outcome:	The proposed model performs significantly worse on long-step problems than short-step ones, with 18 models showing a performance drop of over 50%.

SEGMENT+: Long Text Processing with Short-Context Language Models (2024.emnlp-main)

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Wei Shi, Shuang Li, Kerun Yu, Jinglei Chen, Zujie Liang, Xinhui Wu, Yuxi Qian, Feng Wei, Bo Zheng, Jiaqing Liang, Jiangjie Chen, Yanghua Xiao

Grafting Pre-trained Models for Multimodal Headline Generation (2022.emnlp-industry)

Challenge:	Existing frameworks that increase context window do not guarantee robust performance across long input tasks.
Approach:	They propose a framework that enables language models to handle extended inputs within limited context windows efficiently.
Outcome:	The framework improves performance on long-document question-answering and Needle-in-a-Haystack tasks.

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Lingfeng Qiao, Chen Wu, Ye Liu, Haoyuan Peng, Di Yin, Bo Ren

Enhancing Neural Models with Vulnerability via Adversarial Attack (2020.coling-main)

Challenge:	Existing approaches to generate video headlines with pre-trained language models are labor intensive and impractical.
Approach:	They propose to graft the encoder from the pre-trained video-language model on the generative pre-trainer model and propose a consensus fusion mechanism for the integration of different components.
Outcome:	The proposed model achieves strong results on a brand-new dataset collected from real-world applications.

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Rong Zhang, Qifei Zhou, Bo An, Weiping Li, Tong Mo, Bo Wu

Robustness in NLP

Challenge:	Existing work on adversarial attack to improve performance of NLSM tasks has not been done.
Approach:	They propose a general two-stage training framework to enhance neural models with Vulnerability via adversarial attack.
Outcome:	The proposed framework improves neural models with Vulnerability via adversarial attack on NLSM datasets.

A Survey on LLM-based Conversational User Simulation (2026.eacl-long)

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Bo Ni, Yu Wang, Leyao Wang, Branislav Kveton, Franck Dernoncourt, Yu Xia, Hongjie Chen, Reuben Luera, Samyadeep Basu, Subhojyoti Mukherjee, Puneet Mathur, Nesreen K. Ahmed, Junda Wu, Li Li, Huixin Zhang, Ruiyi Zhang, Tong Yu, Sungchul Kim, Jiuxiang Gu, Zhengzhong Tu, Alexa Siu, Zichao Wang, Seunghyun Yoon, Nedim Lipka, Namyong Park, Zihao Lin, Trung Bui, Yue Zhao, Tyler Derr, Ryan A. Rossi

Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs (2024.findings-emnlp)

Challenge:	Recent advances in large language models (LLMs) have enabled high-fidelity generation of synthetic user conversation.
Approach:	They propose a taxonomy covering user granularity and simulation objectives . they analyze core techniques and evaluation methodologies to help them understand the latest developments .
Outcome:	The proposed model enables high-fidelity generation of synthetic user conversation.

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Zifeng Ding, Jingcheng Wu, Jingpei Wu, Yan Xia, Bo Xiong, Volker Tresp

Structured Data in NLP

Challenge:	Existing models of temporal fact reasoning do not explicitly specify temporal information for each fact.
Approach:	They propose a new type of data structure called hyper-relational TKG to study temporal fact reasoning over HKGs.
Outcome:	The proposed model is based on two new benchmark HTKG datasets . it provides additional key-value pairs (i.e., qualifiers) for each KG fact .

Generative Text Modeling through Short Run Inference (2021.eacl-main)

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Bo Pang, Erik Nijkamp, Tian Han, Ying Nian Wu

Challenge:	Latent variable models for text capture global semantic and syntactic features when trained correctly.
Approach:	They propose a short run dynamics for inference that initializes from the prior distribution of the latent variable and runs a small number of Langevin dynamics steps guided by its posterior distribution.
Outcome:	The proposed model is able to generate coherent sentences with smooth transition and shows no sign of posterior collapse.

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Green & Sustainable NLP

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models (2024.naacl-long)

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Zifeng Ding, Heling Cai, Jingpei Wu, Yunpu Ma, Ruotong Liao, Bo Xiong, Volker Tresp

From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding (2021.acl-long)

Challenge:	Existing methods to forecast links on temporal knowledge graphs are embedding-based . but they face a strong challenge in modeling the unseen zero-shot relations .
Approach:	They propose to embed knowledge graphs (TKGF) entities and relations based on observed contexts into embedding-based methods to model unseen zero-shot relations.
Outcome:	The proposed methods show strong performance on traditional TKG forecasting benchmarks, but they face a strong challenge in modeling unseen zero-shot relations that have no prior graph context.

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Shan Wu, Bo Chen, Chunlei Xin, Xianpei Han, Le Sun, Weipeng Zhang, Jiansong Chen, Fan Yang, Xunliang Cai

Semantic-aware Contrastive Learning for More Accurate Semantic Parsing (2022.emnlp-main)

Challenge:	Experimental results show that Synchronous Semantic Decoding (SSD) can achieve state-of-the-art unsupervised semantic parsing performance on multiple datasets.
Approach:	They propose an unsupervised method which solves the semantic gap and the structure gap by leveraging paraphrasing and grammar-constrained decoding.
Outcome:	The proposed method can solve the semantic gap and structure gap on multiple datasets.

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Shan Wu, Chunlei Xin, Bo Chen, Xianpei Han, Le Sun

KCAT: A Knowledge-Constraint Typing Annotation Tool (P19-3)

Challenge:	Existing studies on semantic parsing use Maximum Likelihood Estimation (MLE) to train discriminative semantic parses.
Approach:	They propose a semantic-aware contrastive learning algorithm which can learn to distinguish fine-grained meaning representations and take the overall sequence-level semantic into consideration.
Outcome:	The proposed algorithm improves on two standard datasets and gets state-of-the-art performance over existing methods.

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Sheng Lin, Luye Zheng, Bo Chen, Siliang Tang, Zhigang Chen, Guoping Hu, Yueting Zhuang, Fei Wu, Xiang Ren

Named Entity Recognition

Challenge:	Recent years Natural Language Processing community has seen a surge of interest in fine-grained entity typing (FET) given an entity mention (i.e. a sequence of token spans representing an entity), FET aims at uncovering its contextdependent type.
Approach:	They propose an efficient Knowledge Constraint Fine-grained Entity Typing Annotation Tool which further improves the entity typing process through entity linking together with some practical functions.
Outcome:	The proposed tool improves the entity typing process by linking the candidate types with some practical functions.

Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training (2021.acl-long)

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Li-Ming Zhan, Haowen Liang, Bo Liu, Lu Fan, Xiao-Ming Wu, Albert Y.S. Lam

Sentiment Analysis

Challenge:	Existing methods for out-of-scope intent detection rely on strong assumptions on data distribution and confidence threshold selection.
Approach:	They propose a method to train an out-of-scope intent classifier in a fully end-to-end manner by simulating the test scenario in training.
Outcome:	The proposed method improves on four benchmark dialogue datasets and improves over state-of-the-art methods.

Video-MMMU: Evaluating Knowledge Acquisition from Multidisciplinary Professional Videos (2026.acl-long)

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Kairui Hu, Penghao Wu, Fanyi Pu, Wang Xiao, Xiang Yue, Bo Li, Yuanhan Zhang, Ziwei Liu

Visual Data in NLP

Challenge:	Existing video benchmarks do not evaluate the knowledge acquisition capabilities of Large Multimodal Models (LMMs) existing video benchmark focuses on static, general visual understanding tasks, without evaluating whether models can acquire knowledge dynamically.
Approach:	They propose a multi-modal, multi-discipline, multitrack benchmark that evaluates Large Multimodal Models’ ability to acquire knowledge from college-level, educational videos.
Outcome:	The proposed benchmark reveals a substantial gap between human learners and current Large Multimodal Models (LMMs) and focuses on improving their learning efficiency.

ShopSimulator: Evaluating and Exploring RL-Driven LLM Agent for Shopping Assistants (2026.acl-long)

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Pei Wang, Yanan Wu, Xiaoshuai Song, Weixun Wang, Gengru Chen, Zhongwen Li, Kezhong Yan, Qi Liu, Ken Deng, Shuaibing Zhao, Shaopan Xiong, Xuepeng Liu, Xuefeng Chen, Wanxi Deng, Wenbo Su, Bo Zheng

Challenge:	Existing studies on large language model-based agents focus on evaluation benchmarks without training support.
Approach:	They propose a large-scale Chinese shopping simulation environment that uses large language models to train agents.
Outcome:	The proposed model performs poorly in a large-scale and challenging shopping environment in China.

Multi-Task Reinforcement Learning for Enhanced Multimodal LLM-as-a-Judge (2026.acl-industry)

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Junjie Wu, Xuan Kan, Zihao He, Shunwen Tan, Bo Pan, Kaitai Zhang

M2PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation (2026.acl-long)

Challenge:	Existing MLLMs are optimized for single-task scenarios and struggle to generalize to diverse contexts.
Approach:	They propose a framework that integrates multitask reinforcement learning and generalization capabilities of MLLMs to optimize the judge model across multiple tasks.
Outcome:	The proposed framework outperforms baseline models in judgment consistency and correlation with human preferences.

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Hao Wang, Linlong Xu, Heng Liu, Yangyang Liu, Xiaohu Zhao, Bo Zeng, Liangying Shao, Yichen Dong, Xinwei Wu, Jiang Zhou, Tianyu Dong, Xiangxiang Zeng, Longyue Wang, Weihua Luo

Machine Translation

Challenge:	prevailing methods for machine translation are often hindered by misleading reward signals.
Approach:	They propose a framework that aligns large language models to human preferences . they propose 'M2PO' to correct the bias towards partial errors .
Outcome:	The proposed framework outperforms open-source models and achieves parity with proprietary models.

Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph (2022.aacl-main)

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Hanning Gao, Lingfei Wu, Po Hu, Zhihua Wei, Fangli Xu, Bo Long

Continual Dialogue State Tracking via Reason-of-Select Distillation (2024.findings-acl)

Challenge:	Existing knowledge graph question answering methods only search for the answer in a large knowledge graph.
Approach:	They propose to partition retrieved knowledge subgraphs into smaller sub-KSGs and then use a graph-augmented learning to rank method to select the top-ranked sub-kSGs.
Outcome:	The proposed method can capture global interactions in question and subgraphs and local interactions on the full KSG and top-ranked sub-KSGs respectively.

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Yujie Feng, Bo Liu, Xiaoyu Dong, Zexin Lu, Li-Ming Zhan, Xiao-Ming Wu, Albert Lam

Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation (2024.acl-long)

Challenge:	Existing research on dialogue systems has focused on domain-specific offline systems lacking adaptation abilities.
Approach:	They propose a Reason-of-Select distillation method that enhances smaller models with a novel "meta-reasoning" capability.
Outcome:	Experiments show that the proposed method significantly improves the performance and generalization capabilities of existing models.

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Wen Wu, Bo Li, Chao Zhang, Chung-Cheng Chiu, Qiujia Li, Junwen Bai, Tara Sainath, Phil Woodland

On-policy Reinforcement Fine-tuning with Offline reward for Multi-step Embodied Planning (2026.acl-long)

Challenge:	Experimental results show that incorporating utterances without majority-agreed labels into an additional class reduces the classification performance of the other emotion classes.
Approach:	They propose to combine utterances without majority-agreed labels into an additional class . they propose to quantify uncertainty in emotion classification using evidential deep learning .
Outcome:	The proposed method retains classification accuracy while effectively detects ambiguous emotion expressions.

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Di Wu, Jiaxin Fan, Chloe Gu, Guanbo Wang, Wei Yin, Wenhao Li, Bo Jin

Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025.acl-long)

Challenge:	Embodied planning requires agents to make coherent multi-step decisions based on dynamic visual observations and verbal goals.
Approach:	They propose an On-policy Reinforcement fine-tuning framework with offline rewards for Embodied Task Planning that preserves generalization benefits of RFT while addressing costly interaction and sparse rewards.
Outcome:	The proposed framework outperforms closed-source and online-RL methods on EmbodiedBench, a recent benchmark for interactive embodied tasks.

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Bo Zeng, Chenyang Lyu, Sinuo Liu, Mingyan Zeng, Minghao Wu, Xuanfan Ni, Tianqi Shi, Yu Zhao, Yefeng Liu, Chenyu Zhu, Ruizhe Li, Jiahui Geng, Qing Li, Yu Tong, Longyue Wang, Weihua Luo, Kaifu Zhang

Multilinguality

Challenge:	Existing datasets for instruction-following are monolingual and centered on English . existing data are unable to capture linguistic and cultural subtle differences .
Approach:	They propose an extension of IFEval to a localized multilingual version called Marco-Bench-MIF . their benchmark addresses linguistic constraints and cultural references via translation and verification .
Outcome:	The proposed extension of IFEval to a localized multilingual version covers 30 languages with varying levels of localization.

AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning (2024.emnlp-main)

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Hao Sun, Jiayi Wu, Hengyi Cai, Xiaochi Wei, Yue Feng, Bo Wang, Shuaiqiang Wang, Yan Zhang, Dawei Yin

Reinforcement Learning on Pre-Training Data (2026.acl-long)

Challenge:	Recent advances in large language models have been remarkable . users face a choice between using cloud-based LLMs for generation quality or local-based ones for lower computational cost .
Approach:	They propose a new LLM utilization paradigm that facilitates collaborative operation . they evaluate AdaSwitch across 7 benchmarks and compare it to other LLMs .
Outcome:	The proposed model improves performance of local and cloud agents across 7 benchmarks . it achieves competitive results compared to the cloud agent while utilizing less computational overhead.

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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang

Intent Contrastive Learning Based on Multi-view Augmentation for Sequential Recommendation (2025.coling-main)

Challenge:	Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings.
Approach:	They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data.
Outcome:	Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base.

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Bo Pei, Yingzheng Zhu, Guangjin Wang, Huajuan Duan, Wenya Wu, Fuyong Xu, Yizhao Zhu, Peiyu Liu, Ran Lu

Diagram-Driven Course Questions Generation (2025.emnlp-main)

Challenge:	Existing work on intent-related models fails to capture long-term dependencies in user behavior and fails to effectively utilize item relevance.
Approach:	They propose a sequential recommendation framework that combine temporal variability with position encoding that has extrapolation properties to encode sequences, thereby expanding the model’s view of user behavior.
Outcome:	The proposed model improves on three real datasets by 0.8% to 14.7% compared to baselines.

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Xinyu Zhang, Lingling Zhang, Yanrui Wu, Muye Huang, Wenjun Wu, Bo Li, Shaowei Wang, Basura Fernando, Jun Liu

Visual Data in NLP

Challenge:	Visual Question Generation (VQG) research focuses on natural images while neglecting diagrams, a critical component of educational materials.
Approach:	They propose a diagram-driven course questions generation task to generate diagram-relevant questions for specific courses.
Outcome:	The proposed framework outperforms existing models on DiagramQG while maintaining strong generalizability across natural image datasets.

VI-OOD: A Unified Framework of Representation Learning for Textual Out-of-distribution Detection (2024.lrec-main)

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Li-Ming Zhan, Bo Liu, Xiao-Ming Wu

VideoQA-TA: Temporal-Aware Multi-Modal Video Question Answering (2025.coling-main)

Challenge:	Out-of-distribution (OOD) detection is a crucial part of deep neural networks.
Approach:	They propose a variational inference framework which maximizes the likelihood of the joint distribution p(x, y) instead of p[y\|x).
Outcome:	The proposed framework maximizes the likelihood of the joint distribution p(x, y) instead of p[y\|x).

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Zhixuan Wu, Bo Cheng, Jiale Han, Jiabao Ma, Shuhao Zhang, Yuli Chen, Changbo Li

Eureka: Neural Insight Learning for Knowledge Graph Reasoning (2022.coling-1)

Challenge:	Existing methods for video question answering align visual or textual features directly with large language models, limiting the deep semantic association between modalities and hindering a comprehensive understanding of interactions within spatial and temporal contexts.
Approach:	They propose a temporal-aware framework for multi-modal video question answering that aligns videos and questions at fine-grained levels.
Outcome:	The proposed framework improves reasoning ability and accuracy of videoQA by aligning videos and questions at fine-grained levels.

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Alex X. Zhang, Xun Liang, Bo Wu, Xiangping Zheng, Sensen Zhang, Yuhui Guo, Jun Wang, Xinyao Liu

Structured Data in NLP

Challenge:	Existing knowledge embedding methods have limited performance on knowledge graph reasoning tasks . eureka is empowered to learn seen relations with sufficient training triples .
Approach:	They propose a neural insight learning framework called Eureka to bridge the “seen” to “unsea” gap . Eureca is empowered to learn seen relations with sufficient training triples while providing flexibility to learn unseen relations given only one trigger .
Outcome:	The proposed framework outperforms state-of-the-art models on seen and unseen relations . it can learn seen and unseen relationships with sufficient training triples .

Selective Prompting Tuning for Personalized Conversations with LLMs (2024.findings-acl)

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Qiushi Huang, Xubo Liu, Tom Ko, Bo Wu, Wenwu Wang, Yu Zhang, Lilian Tang

Low-Resource NLP

Challenge:	Personalization in conversational AI requires persona profiles and contextual understanding to create meaningful conversations.
Approach:	They propose a method that softly prompts LLMs for personalized conversations in a selective way.
Outcome:	The proposed approach improves response diversity by up to 90% on the CONVAI2 dataset.

HyperAdaLoRA: Accelerating LoRA Rank Allocation During Training via Hypernetworks without Sacrificing Performance (2026.findings-acl)

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Hao Zhang, Zhenjia Li, Yifan Gao, Xi Xiao, Heng Zhang, Shuyang Zhang, null Xiaoxincc, Bo Huang, Yuhang Wu, Tianyang Wang, Hao Xu

SparkRA: A Retrieval-Augmented Knowledge Service System Based on Spark Large Language Model (2024.emnlp-demo)

Challenge:	Low-Rank Adaptation (LoRA) assumes a uniform rank r for each incremental matrix, not accounting for the varying significance of weight matrices across modules and layers.
Approach:	They propose a framework that allows for faster convergence of low-rank adaptive models . they use a hypernetwork to prune the outputs of the hypernetworks to generate parameters .
Outcome:	The proposed framework accelerates convergence of AdaLoRA by leveraging a hypernetwork.

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Dayong Wu, Jiaqi Li, Baoxin Wang, Honghong Zhao, Siyuan Xue, Yanjie Yang, Zhijun Chang, Rui Zhang, Li Qian, Bo Wang, Shijin Wang, Zhixiong Zhang, Guoping Hu

Automatic Scene-based Topic Channel Construction System for E-Commerce (2022.emnlp-industry)

Challenge:	Large language models (LLMs) have shown remarkable achievements across various language tasks.
Approach:	They propose a scientific literature LLM and a knowledge service system based on it . they collect scientific literature and then pre-train it using autoregressive training .
Outcome:	The proposed system provides literature investigation, paper reading, and academic writing functions.

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Peng Lin, Yanyan Zou, Lingfei Wu, Mian Ma, Zhuoye Ding, Bo Long

MMSearch-R1: Incentivizing LMMs to Search (2026.acl-long)

Challenge:	Recent scene marketing has proved effective for offline shopping.
Approach:	They propose a novel product form, scene-based topic channel, which consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words.
Outcome:	The proposed system can be automated and tested on a real-world e-commerce recommendation platform.

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Jinming Wu, Zihao Deng, Wei Li, Yiding Liu, Bo You, Bo Li, Zejun MA, Ziwei Liu

How to leverage the multimodal EHR data for better medical prediction? (2021.emnlp-main)

Challenge:	Existing approaches to deploying large multimodal models rely on rigid pipelines . Existing methods such as retrieval-augmented generation and prompt engineered search rely only on rigid knowledge sources.
Approach:	They propose a framework that enables LMMs to perform on-demand, multi-turn search in real-world Internet environments.
Outcome:	The proposed model outperforms existing models while reducing search calls by over 30%.

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Bo Yang, Lijun Wu

Towards LLM-driven Dialogue State Tracking (2023.emnlp-main)

Challenge:	Using deep learning to improve healthcare is challenging due to the complexity of EHR data.
Approach:	They propose a method to integrate clinical notes from EHR and combine them with different data to improve prediction performance.
Outcome:	The proposed model outperforms the state-of-the-art method without clinical notes on two prediction tasks.

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Yujie Feng, Zexin Lu, Bo Liu, Liming Zhan, Xiao-Ming Wu

Targeting the Needle, Ignoring the Haystack: Anchoring Crucial Cues for Evolving Scam Call Detection via an LLM-Assisted Classifier (2026.findings-acl)

Challenge:	emergence of large language models (LLMs) such as GPT3 and ChatGPT has sparked considerable interest in assessing their efficacy across diverse applications.
Approach:	They present a framework for a domain-slot instruction tuning method that allows LDST to achieve performance on par with ChatGPT.
Outcome:	The proposed framework performs better in zero-shot and few-shot settings than previous SOTA methods.

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Tong Wu, Qinliang Su, Jianxing Yu, Bo Liang, Minhua Huang

Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning (2026.findings-acl)

Challenge:	Existing methods for fraud detection on online service platforms often fail to generalize due to the scarcity of labeled data and the continuous evolution of conversational contexts.
Approach:	They propose a framework that anchors detection on Semantic Primitives . they prioritize stable evidence over conversational noise to ensure a verifiable fraud tactic .
Outcome:	The proposed framework achieves superior robustness and efficiency compared to baselines . it prioritizes stable evidence over diverse conversational noise .

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Zhiyin Yu, Bo Zhang, Qibin Hou, Zhonghai Wu, Xiao Luo, Lei Bai

Entity-centered Cross-document Relation Extraction (2022.emnlp-main)

Challenge:	Experimental results show that EasyRL consistently outperforms state-of-the-art baselines due to the substantial annotation cost and issues such as model collapse or reward hacking.
Approach:	They propose a supervised RL approach with a divide-and-conquer strategy that simulates the human cognitive acquisition curve using easy labeled data.
Outcome:	The proposed approach outperforms state-of-the-art models on mathematical and scientific benchmarks using only 10% of easy labeled data.

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Fengqi Wang, Fei Li, Hao Fei, Jingye Li, Shengqiong Wu, Fangfang Su, Wenxuan Shi, Donghong Ji, Bo Cai

Constructing contrastive samples via summarization for text classification with limited annotations (2021.findings-emnlp)

Challenge:	Existing methods for relation extraction only use text snippets surrounding target entities in multiple documents.
Approach:	They propose a relation-extraction model that uses cross-path entity relation attention to detect the semantic relations between entities in a given text.
Outcome:	The proposed method outperforms the state-of-the-art methods in the dataset CodRED by 10%.

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Yangkai Du, Tengfei Ma, Lingfei Wu, Fangli Xu, Xuhong Zhang, Bo Long, Shouling Ji

Continuous Relational Diffusion Driven Topic Model with Multi-grained Text for Microblog (2024.lrec-main)

Challenge:	Various contrastive learning methods have been developed and lead to state-of-the-art performance in many computer vision tasks.
Approach:	They propose a method to construct efficient contrastive samples using text summarization to gain better representations of text classification tasks with limited annotations.
Outcome:	The proposed framework gains better representations on text classification tasks with limited annotations and is compared with existing methods on real-world text classification datasets.

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Chenhao Wu, Ruifang He, Chang Liu, Bo Wang

Select Before Use: On the Importance of Reference Model Selection in Preference Alignment (2026.acl-long)

Challenge:	Existing topic models assume that there are only 0/1-state relationships between the two parties in social networks, but the relationship status in real life is more complicated.
Approach:	They propose a topic model that leverages unsupervised learning to mine hidden topics in document collections using multi-grained text.
Outcome:	The proposed model can be applied to microblog with multi-grained text to realize the representation of the relationship state and make up for the context and structural information lost by previous representation methods.

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Muyang Li, Runze Wu, Xiangyu Zhao, Bo Han, Daoyi Dong, Tongliang Liu

Challenge:	Supervised Fine-Tuning (SFT) is used as the initialization and reference model for subsequent preference alignment.
Approach:	They propose to use RewardRank to estimate initial implicit alignment between reference model and preference objective to ensure LLMs generate safe, helpful, and instruction-aligned content.
Outcome:	Empirical evidence shows that using the selected model as reference can gain up to 67.6% relative increase on length-controlled win rate compared to baselines.

Synergizing Multimodal Temporal Knowledge Graphs and Large Language Models for Social Relation Recognition (2025.emnlp-main)

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Haorui Wang, Zheng Wang, Yuxuan Zhang, Bo Wang, Bin Wu

EasyGen: Easing Multimodal Generation with BiDiffuser and LLMs (2024.acl-long)

Challenge:	Recent advances in Large Language Models (LLMs) have limited capacity to model complex graph-structured relationships.
Approach:	They propose a low-coupling method synergizing multimodal temporal Knowledge Graphs and Large Language Models for social relation reasoning.
Outcome:	The proposed method exhibits state-of-the-art performance in social relation recognition . it bridges the gap between KGs and LLMs and will be released after acceptance .

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Xiangyu Zhao, Bo Liu, Qijiong Liu, Guangyuan Shi, Xiao-Ming Wu

Closing the Spatial Execution Gap in Digital Whiteboards via Verifiable Reinforcement Learning (2026.acl-long)

Challenge:	Existing multimodal models that depend on encoders like CLIP or ImageBind need ample amounts of training data to bridge modalities.
Approach:	They propose an efficient model that leverages bidirectional conditional diffusion model to foster more efficient modality interactions.
Outcome:	The proposed model is able to train a projection layer linking an LLM and an adapter to align the LLM’s text space with the bidirectional diffusion model.

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Chang Liu, Benjamin Wagley, Zibo Wang, Mehmet E. Belviranli, Bo Wu

A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)

Challenge:	Large language models suffer from a fundamental Spatial Execution Gap, failing to translate visual semantics into precise, schema-valid coordinate operations in interactive environments.
Approach:	They propose a pipeline that leverages Group Relative Policy Optimization to enforce a strict Identify-Reason-Verify protocol and train on execution-verifiable rewards.
Outcome:	The proposed pipeline outperforms a state-of-the-art frontier model by 16.75% in operation accuracy.

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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo

Robust Transfer Learning with Pretrained Language Models through Adapters (2021.acl-short)

Challenge:	Existing research on reinforcement learning for LLMs under data scarcity has not been unified.
Approach:	They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric.
Outcome:	The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area.

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Wenjuan Han, Bo Pang, Ying Nian Wu

GAIA: A Fine-grained Multimedia Knowledge Extraction System (2020.acl-demos)

Challenge:	Existing approaches to transfer learning with pretrained transformer-based language models are not robust and can be adversarial.
Approach:	They propose a simple yet effective adapter-based approach to fine-tune language models on downstream tasks.
Outcome:	The proposed approach improves stability and adversarial robustness in transfer learning to various downstream tasks.

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Manling Li, Alireza Zareian, Ying Lin, Xiaoman Pan, Spencer Whitehead, Brian Chen, Bo Wu, Heng Ji, Shih-Fu Chang, Clare Voss, Daniel Napierski, Marjorie Freedman

Visual Data in NLP

Challenge:	Open source knowledge extraction tools are used for many real-world applications, but there is no comprehensive system for KE.
Approach:	They propose a multimedia knowledge extraction system that takes multimedia data from various sources and languages as input and creates a coherent, structured knowledge base.
Outcome:	The system achieves top performance at the recent NIST TAC SM-KBP2019 evaluation.

Noise Learning for Text Classification: A Benchmark (2022.coling-1)

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Bo Liu, Wandi Xu, Yuejia Xiang, Xiaojun Wu, Lejian He, Bowen Zhang, Li Zhu

Adaptive Reinforcement Tuning Language Models as Hard Data Generators for Sentence Representation (2024.lrec-main)

Challenge:	Existing noise learning methods for text classification are underdeveloped . authors propose a noise learning benchmark for text classification .
Approach:	They propose to use four state-of-the-art methods of noise learning from the image domain to classify text.
Outcome:	The proposed benchmark of noise learning for text classification is based on four methods and five noise modes.

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Bo Xu, Yifei Wu, Shouang Wei, Ming Du, Hongya Wang

TaSL: Continual Dialog State Tracking via Task Skill Localization and Consolidation (2024.acl-long)

Challenge:	Existing methods use contrastive learning (CL) to learn effective sentence representations, but require extensive human annotation.
Approach:	They propose a reinforcement learning approach for fine-tuning small-parameter LLMs to generate high-quality hard contrastive data without human feedback.
Outcome:	The proposed method achieves state-of-the-art on seven semantic text similarity tasks.

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Yujie Feng, Xu Chu, Yongxin Xu, Guangyuan Shi, Bo Liu, Xiao-Ming Wu

Do not Abstain! Identify and Solve the Uncertainty (2025.acl-long)

Challenge:	Current methods for Continual Dialogue State Tracking (DST) struggle with catastrophic forgetting and knowledge transfer between tasks.
Approach:	They propose a framework for task skill localization and consolidation that enables effective knowledge transfer without relying on memory replay.
Outcome:	The proposed framework shows a 7.6% increase in Avg. JGA and 11% rise in BWT metrics over existing state-of-the-art methods.

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Jingyu Liu, JingquanPeng JingquanPeng, Xiaopeng Wu, Xubin Li, Tiezheng Ge, Bo Zheng, Yong Liu

Challenge:	Existing solutions rely on evasive responses when confronting uncertain scenarios.
Approach:	They propose a benchmark to assess LLMs' ability to recognize and address uncertainty . they generate context-aware inquiries that highlight the confusing aspect of the original query .
Outcome:	Experiments with ConfuseBench show that LLMs struggle to identify root cause of uncertainty and solve it.

IDEA: An Interpretable and Editable Decision-Making Framework for LLMs via Verbal-to-Numeric Calibration (2026.findings-acl)

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Yanji He, Yuxin Jiang, Yiwen Wu, Bo Huang, Jiaheng Wei, Wei Wang

Explainability & Interpretability in NLP

Challenge:	Existing approaches fail to integrate domain expert insights beyond simple prompting.
Approach:	They propose a framework that extracts LLM decision knowledge into an interpretable parametric model over semantically meaningful factors.
Outcome:	Experiments show that IDEA outperforms DeepSeek R1 and GPT-5.2 in accuracy and accuracy.

ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models (2024.findings-acl)

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Yanan Wu, Jie Liu, Xingyuan Bu, Jiaheng Liu, Zhanhui Zhou, Yuanxing Zhang, Chenchen Zhang, ZhiqiBai ZhiqiBai, Haibin Chen, Tiezheng Ge, Wanli Ouyang, Wenbo Su, Bo Zheng

Reasoning

Challenge:	ConceptMath evaluates concept-wise mathematical reasoning of Large Language Models (LLMs) Existing benchmarks that evaluate general mathematical reasoning with an average accuracy fail to probe the fine-grained failure modes of mathematical reasoning on specific datasets.
Approach:	They introduce a bilingual, fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models.
Outcome:	The proposed benchmarks evaluate concept-wise mathematical reasoning of Large Language Models with concept-based accuracies.

How Good Are LLMs at Out-of-Distribution Detection? (2024.lrec-main)

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Bo Liu, Li-Ming Zhan, Zexin Lu, Yujie Feng, Lei Xue, Xiao-Ming Wu

Feeding What You Need by Understanding What You Learned (2022.acl-long)

Challenge:	Out-of-distribution (OOD) detection is crucial for ensuring AI safety . large language models (LLMs) are becoming more prevalent due to their scale, pre-training objectives, and paradigms used for inference.
Approach:	They propose to use large language models to investigate out-of-distribution (OOD) detection in machine learning.
Outcome:	The proposed method outperforms other OOD detectors in zero-grad and fine-tuning scenarios.

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Xiaoqiang Wang, Bang Liu, Fangli Xu, Bo Long, Siliang Tang, Lingfei Wu

Green & Sustainable NLP

Challenge:	Existing research on machine reading comprehension rely heavily on large-size models and corpus to improve performance.
Approach:	They propose a framework that assesses model capabilities in an explainable and multi-dimensional manner.
Outcome:	The proposed framework achieves an 11.22% / 8.71% improvement of EM / F1 on MRC tasks.

M2RC-EVAL: Massively Multilingual Repository-level Code Completion Evaluation (2025.acl-long)

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Jiaheng Liu, Ken Deng, Congnan Liu, Jian Yang, Shukai Liu, He Zhu, Peng Zhao, Linzheng Chai, Yanan Wu, JinKe JinKe, Ge Zhang, Zekun Moore Wang, Guoan Zhang, Yingshui Tan, Bangyu Xiang, Zhaoxiang Zhang, Wenbo Su, Bo Zheng

Programming Languages in NLP

Challenge:	Existing repository-level code completion benchmarks focus on a limited number of languages . existing benchmarks report overall average scores of different languages ignoring fine-grained abilities .
Approach:	They propose to use repository-level code completion benchmarks to evaluate general code intelligence abilities across languages for existing code Large Language Models.
Outcome:	The proposed benchmarks improve the code completion abilities of existing LLMs by using two types of annotations on the parsed syntax tree.

Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models (2025.acl-long)

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Huifeng Yin, Yu Zhao, Minghao Wu, Xuanfan Ni, Bo Zeng, Huaiyu.wh Huaiyu.wh, Tianqi Shi, Liangying Shao, Chenyang Lyu, Longyue Wang, Weihua Luo, Kaifu Zhang

GraphNarrator: Generating Textual Explanations for Graph Neural Networks (2025.acl-long)

Challenge:	Recent efforts to distill large reasoning models into smaller lightweight models have shown competitive performances.
Approach:	They propose to distill long Chain-of-Thought data to improve SFT and RL methods by constructing data from scratch using Monte Carlo Tree Search.
Outcome:	The proposed method significantly improves reasoning performance on various benchmarks such as math (GSM8K, MATH, AIME).

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Bo Pan, Zhen Xiong, Guanchen Wu, Zheng Zhang, Yifei Zhang, Yuntong Hu, Liang Zhao

LIME: Less Is More for MLLM Evaluation (2025.findings-acl)

Challenge:	Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis.
Approach:	They propose to use a generative language model to map input-output pairs to explanations reflecting the model’s decision-making process to generate a model that generates pseudo-labels that capture the model's decisions from saliency-based explanations.
Outcome:	Extensive experiments show that GraphNarrator produces human-preferred explanations that are faithful, concise, and human-like.

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King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang

Learning from Children: Improving Image-Caption Pretraining via Curriculum (2023.findings-acl)

Challenge:	Existing MLLM benchmarks and unified evaluation frameworks cannot accurately and efficiently reflect the ability of MLMLs.
Approach:	They propose a semi-automated benchmark curated using a pipeline that filters out uninformative samples and eliminates answer leakage by focusing on tasks that require image-based understanding.
Outcome:	The proposed benchmark reduces the number of samples by 76% and evaluation time by 77% while it can more effectively distinguish different models’ abilities.

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Hammad Ayyubi, Rahul Lokesh, Alireza Zareian, Bo Wu, Shih-Fu Chang

Cache-of-Thought: Master-Apprentice Framework for Cost-Effective Vision Language Model Reasoning (2025.emnlp-main)

Challenge:	Image-caption pretraining is a difficult problem as it requires multiple concepts (nouns) from captions to be aligned to multiple objects in images.
Approach:	They propose a curriculum learning framework that uses images to align multiple concepts to multiple objects in an image.
Outcome:	The proposed learning framework improves over pretraining from scratch, using a pretrained image or/and text encoder, low data regime etc.

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Mingyuan Wu, Jize Jiang, Haozhen Zheng, Meitang Li, Zhaoheng Li, Beitong Tian, Bo Chen, Yongjoo Park, Minjia Zhang, ChengXiang Zhai, Klara Nahrstedt

Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering (2026.findings-eacl)

Challenge:	Recent Vision Language Models (VLMs) have shown tremendous promise in a wide range of realworld applications, but their size has made at-scale deployment and operation challenging due to high consumption of cloud computing resource, high latency, and expensive API calls.
Approach:	They propose a master–apprentice framework for collaborative inference between large and small vision language models.
Outcome:	The proposed framework improves reasoning performance on widely-recognized and challenging general reasoning benchmarks and specifically boosts reasoning of apprentice VLMs by 36.6%.

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Haiyan Zhao, Xuansheng Wu, Fan Yang, Bo Shen, Ninghao Liu, Mengnan Du

ProgCo: Program Helps Self-Correction of Large Language Models (2025.acl-short)

Challenge:	Existing methods for steering concept vectors suffer from noisy features in diverse datasets that undermine steering robustness.
Approach:	They propose a Sparse Autoencoder-Denoised Concept Vector (SDCV) which selectively keeps the most discriminative SAE latents while reconstructing hidden representations.
Outcome:	The proposed method improves steering success rates by 4-16% across six challenging concepts while maintaining topic relevance.

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Xiaoshuai Song, Yanan Wu, Weixun Wang, Jiaheng Liu, Wenbo Su, Bo Zheng

Improving Robustness of GNN-based Anomaly Detection by Graph Adversarial Training (2024.lrec-main)

Challenge:	Existing LLMs fail to self-correct and generate correct feedback, leading to misleading refinement and failure of self-refinement.
Approach:	They propose a program-driven self-correction approach that uses program-based verification to self-refine initial responses without external feedback.
Outcome:	The proposed model achieves self-correction and can be further enhanced when combined with real program tools.

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Xiangping Zheng, Bo Wu, Alex X. Zhang, Wei Li

Hypergraph-Based Session Modeling: A Multi-Collaborative Self-Supervised Approach for Enhanced Recommender Systems (2024.lrec-main)

Challenge:	Graph neural networks excel at anomaly detection, but exhibit vulnerability to attacks . novel mechanism for graph adversarial training designed to bolster anomaly detectors .
Approach:	They propose a mechanism for graph adversarial training to bolster anomaly detection systems against potential poisoning attacks.
Outcome:	The proposed method bolsters GNN-based anomaly detection systems against poisoning attacks.

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Xiangping Zheng, Bo Wu, Alex X. Zhang, Wei Li

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

Challenge:	Presently, graph-based recommendations are limited by session dependencies and data sparsity in real-world scenarios.
Approach:	They propose a method which uses multi-collaborative self-supervised learning in hypergraph neural networks to model item transitions and to mitigate the challenges of data sparsity.
Outcome:	The proposed method outperforms existing methods in a number of domains and consistently outperformed existing methods.

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

Reasoning

Challenge:	Parallel thinking is a promising avenue for scaling test-time compute in Large Language Models . however, coordinating the exploration and aggregation stages remains challenging .
Approach:	They propose a parallel thinking framework that explicitly incentivizes coordination between components via end-to-end reinforcement learning.
Outcome:	The proposed framework improves accuracy by 6.0% over long chain-of-thought baselines while reducing wall-clock latency by 39.4% under matched token budgets.

WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition (D18-1)

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Yufeng Diao, Hongfei Lin, Di Wu, Liang Yang, Kan Xu, Zhihao Yang, Jian Wang, Shaowu Zhang, Bo Xu, Dongyu Zhang