Papers by Bo Zhang

Temporal Knowledge Graph Reasoning with Dynamic Hypergraph Embedding (2024.lrec-main)

Challenge:	Large language models have created significant safety concerns . factuality ability is crucial in determining whether they can be deployed and applied safely and compliantly within specific regions.
Approach:	They propose a benchmark to evaluate the factuality of large language models in China . they evaluate the models' ability to provide accurate and reliable information .
Outcome:	The proposed benchmark evaluates the factuality abilities of existing LLMs and compares them to LLM abilities.

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Xinyue Liu, Jianan Zhang, Chi Ma, Wenxin Liang, Bo Xu, Linlin Zong

GeoGPT4V: Towards Geometric Multi-modal Large Language Models with Geometric Image Generation (2024.emnlp-main)

Challenge:	Existing models that model temporal dynamics with knowledge graphs and graph convolution networks lack high-order interactions between objects in TKG, which is an important factor to predict future facts.
Approach:	They propose to embed temporal knowledge graph reasoning by constructing hypergraphs based on temporal information graphs at different timestamps and then adapt dynamic meta-embedding to fit TKG.
Outcome:	The proposed method outperforms baseline models on public TKG datasets and provides good interpretation for the predicted results.

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Shihao Cai, Keqin Bao, Hangyu Guo, Jizhi Zhang, Jun Song, Bo Zheng

Dolphin: Moving Towards Closed-loop Auto-research through Thinking, Practice, and Feedback (2025.acl-long)

Challenge:	Existing datasets are too challenging for direct model learning or suffer from misalignment between text and images.
Approach:	They propose a pipeline that leverages GPT-4 and GPT4V to generate geometry problems with aligned text and images, facilitating model learning.
Outcome:	The proposed pipeline generates 4.9K geometry problems with aligned text and images, facilitating model learning.

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Jiakang Yuan, Xiangchao Yan, Bo Zhang, Tao Chen, Botian Shi, Wanli Ouyang, Yu Qiao, Lei Bai, Bowen Zhou

Challenge:	Recent studies show that AI-assisted research methods can improve research efficiency . a closed-loop framework is used to enhance the automation level of scientific research .
Approach:	They propose a closed-loop LLM-driven framework to enhance the automation level of scientific research.
Outcome:	The proposed framework improves the efficiency of scientific research by improving data analysis, accelerating computation, and fostering novel idea generation.

MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs (2026.acl-long)

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Xiangyu Zhao, Wanghan Xu, Bo Liu, Yuhao Zhou, Fenghua Ling, Ben Fei, Xiaoyu Yue, Lei Bai, Wenlong Zhang, Xiao-Ming Wu

E2-LLM: Efficient and Extreme Length Extension of Large Language Models (2024.findings-acl)

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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Jiaheng Liu, ZhiqiBai ZhiqiBai, Yuanxing Zhang, Chenchen Zhang, YuangZh YuangZh, Ge Zhang, JiakaiWang JiakaiWang, Haoran Que, Yukang Chen, Wenbo Su, Tiezheng Ge, Jie Fu, Wenhu Chen, Bo Zheng

All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG (2026.acl-long)

Challenge:	Existing techniques for extending context capabilities in LLMs require additional training procedures and access to datasets with long context (e.g., sequences of 32K tokens).
Approach:	They propose a solution to extend context capabilities in Large Language Models by training a single process over a sequence of 4K tokens.
Outcome:	The proposed solution significantly reduces the cost of continual-pretraining or fine-tuning over short sequences and improves robustness to diverse relative positions.

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Dan Wang, Guozhao Mo, Yafei Shi, Cheng Zhang, Bo Zheng, Boxi Cao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Le Sun

Estimating Privacy Leakage of Augmented Contextual Knowledge in Language Models (2025.acl-long)

Challenge:	Existing mRAG systems suffer from a language bias during reranking, systematically favoring English and the query’s native language.
Approach:	They propose a language-agnostic utility-driven reranker alignment technique to mitigate language bias during re-ranking.
Outcome:	The proposed approach mitigates language bias and consistently improves mRAG performance across languages.

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James Flemings, Bo Jiang, Wanrong Zhang, Zafar Takhirov, Murali Annavaram

Ethical NLP

Challenge:	Prior work has shown that privacy leakage of parametric knowledge often occurs from memorized pre-training data.
Approach:	They propose a metric that builds on differential privacy to estimate the privacy leakage of contextual knowledge during decoding by comparing parametric and contextual knowledge.
Outcome:	The proposed method overestimates the privacy leakage of parametric knowledge while separating parametric and contextual knowledge.

USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS (2026.acl-long)

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Baolin Zheng, Guanlin Chen, Qingyang Teng, Hongqiong Zhong, Yingshui Tan, Zhendong Liu, Weixun Wang, Jiaheng Liu, Jian Yang, Huiyun Jing, Jincheng Wei, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang

A Generative Pre-Trained Language Model for Channel Prediction in Wireless Communications Systems (2025.emnlp-main)

Challenge:	Existing safety benchmarks fail to provide reliable assessments due to limited risk coverage, insufficient scale and the oversight of complex modality combinations.
Approach:	They propose a framework that covers 61 risk categories across four modality interactions to address this gap.
Outcome:	The proposed framework covers 61 risk categories across four distinct modality interactions.

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Bo Lin, Huanming Zhang, Yuhua Jiang, Yucong Wang, Tengyu Zhang, Shaoqiang Yan, Hongyao Li, Yihong Liu, Feifei Gao

Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference (2022.coling-1)

Challenge:	Existing model-based channel prediction methods suffer from limited accuracy due to imperfect temporal modeling, while existing AI-based methods suffers from limited generalization due to inadequate training strategies.
Approach:	They propose a generative pre-trained language model for channel prediction based on channel correlation and train it based upon transformer decoder architecture.
Outcome:	The proposed model can learn various channel characteristics and perform impressive tasks across multiple dimensions.

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Guanglin Niu, Bo Li, Yongfei Zhang, Shiliang Pu

RWKV: Reinventing RNNs for the Transformer Era (2023.findings-emnlp)

Challenge:	Existing knowledge graphs are incomplete and therefore lack interpretability.
Approach:	They propose a closed-loop neural-symbolic learning framework EngineKG to address the natural incompleteness of knowledge graphs.
Outcome:	The proposed model outperforms baselines on link prediction tasks on four real-world datasets.

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Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu

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

Challenge:	recurrent neural networks struggle to match the performance of Transformers due to limitations in parallelization and scalability.
Approach:	They propose a model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs.
Outcome:	The proposed model performs on par with similarly sized RNNs, suggesting future work can leverage this architecture to create more efficient models.

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

A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents (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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Qingrong Xia, Bo Zhang, Rui Wang, Zhenghua Li, Yue Zhang, Fei Huang, Luo Si, Min Zhang

Rethinking Smoothness for Fast and Adaptable Entity Alignment Decoding (2025.findings-naacl)

Challenge:	Existing methods for fine-grained opinion mining (OM) are based on span-based annotations, but they are not effective.
Approach:	They propose a unified span-based approach for the end-to-end OM setting using syntactic constituents and multi-task learning to integrate them into the proposed model.
Outcome:	The proposed approach achieves significant improvements over previous work on the MPQA 2.0 dataset and reduces the number of wrongly-predicted opinion expressions and roles.

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Yuanyi Wang, Han Li, Haifeng Sun, Lei Zhang, Bo He, Wei Tang, Tianhao Yan, Qi Qi, Jingyu Wang

FlowRAG: Synergizing Explicit Reasoning via Frequency-Aware Multi-Granularity Graph Flow (2026.findings-acl)

Challenge:	Existing methods for integrating knowledge graphs rely on entity and relation embeddings . Fig. 1 shows how to decode knowledge graph in under 6 seconds .
Approach:	They propose a framework that only utilizes entity embeddings to decode knowledge graphs.
Outcome:	The proposed framework reconstructs KG representation by maximizing smoothness of entity embeddings.

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Bihao Zhan, Zongsheng Cao, Jie Zhou, Bo Zhang, Liang He

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

Challenge:	Existing methods for Graph-based retrieval-augmented generation rely on implicit semantic relevance propagation.
Approach:	They propose a semantic-aware retrieval framework that improves both semantic recall and explicit reasoning.
Outcome:	Extensive experiments show that FlowRAG improves both semantic recall and explicit reasoning.

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

Towards Better Utilization of Multi-Reference Training Data for Chinese Grammatical Error Correction (2024.findings-acl)

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%.

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Yumeng Liu, Zhenghua Li, HaoChen Jiang, Bo Zhang, Chen Li, Ji Zhang

The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)

Challenge:	a high proportion of Chinese training data is multi-referenced for the grammatical error correction task . however, there are many ways to correct an erroneous input sentence . a systematic study on multi-referencing training data has been proposed .
Approach:	They propose two new approaches and a simple two-stage training strategy to better utilize multi-reference training data.
Outcome:	The proposed methods show that Chinese training data contain multiple references.

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Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen

Dynamic Parallel Tree Search for Efficient LLM Reasoning (2025.acl-long)

Challenge:	Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy.
Approach:	They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges.
Outcome:	The proposed framework reduces retrieval time while maintaining high model performance.

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Yifu Ding, Wentao Jiang, Shunyu Liu, Yongcheng Jing, Jinyang Guo, Yingjie Wang, Jing Zhang, Zengmao Wang, Ziwei Liu, Bo Du, Xianglong Liu, Dacheng Tao

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

Challenge:	Recent methods focus on search accuracy while overlooking computational efficiency.
Approach:	They propose a parallelism framework that dynamically optimizes reasoning path in inference.
Outcome:	The proposed framework improves efficiency by 2-4 on average while maintaining or even surpassing existing reasoning algorithms in accuracy.

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

Correctable-DST: Mitigating Historical Context Mismatch between Training and Inference for Improved Dialogue State Tracking (2022.emnlp-main)

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.

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Hongyan Xie, Haoxiang Su, Shuangyong Song, Hao Huang, Bo Zou, Kun Deng, Jianghua Lin, Zhihui Zhang, Xiaodong He

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

Challenge:	Existing dialogue state tracking approaches predict the dialogue state of a target turn sequentially based on the ground-truth previous dialogue state.
Approach:	They propose a method that predicts dialogue state sequentially based on previous dialogue state . they propose generating a previously “predicted” dialogue state using ground-truth previous dialogue states .
Outcome:	The proposed method achieves 67.51%, 68.24%, 70.30%, 71.38%, and 81.27% joint goal accuracy on MultiWOZ 2.0-2.4 datasets.

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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

UFO: A UI-Focused Agent for Windows OS Interaction (2025.naacl-long)

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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Chaoyun Zhang, Liqun Li, Shilin He, Xu Zhang, Bo Qiao, Si Qin, Minghua Ma, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

Automatic ICD Coding Exploiting Discourse Structure and Reconciled Code Embeddings (2022.coling-1)

Challenge:	UFO is a UI-Fcused agent designed to fulfill user requests tailored to Windows OS applications . it decomposes user requests using divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications.
Approach:	They propose a UI-Fcused Windows OS agent that decomposes user requests using a divide-and-conquer approach and incorporates a control interaction module tailored for Windows OS.
Outcome:	The proposed agent decomposes user requests using divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications.

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Shurui Zhang, Bozheng Zhang, Fuxin Zhang, Bo Sang, Wanchun Yang

Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLMs-Powered Assistance (2024.acl-long)

Challenge:	Existing studies did not exploit the discourse structure of clinical notes, which provides rich contextual information for code assignment.
Approach:	They propose to leverage section type classification and section type embeddings to exploit the discourse structure of clinical notes to generate rich contextual information for code assignment.
Outcome:	The proposed model outperforms state-of-the-art models on a MIMIC dataset by a large margin.

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Bo Yuan, Yulin Chen, Yin Zhang, Wei Jiang

Unifying Inference-Time Planning Language Generation (2026.findings-acl)

Challenge:	Existing methods for learning from noisy labels are difficult to improve . existing methods identify noisy labels and use active learning to query experts .
Approach:	They propose a collaborative learning framework to combine LLMs and small models for learning from noisy labels.
Outcome:	The proposed framework outperforms state-of-the-art baselines on synthetic and real-world noise datasets.

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Prabhu Prakash Kagitha, Bo Sun, Ishan Desai, Andrew Zhu, Cassie Huang, Manling Li, Ziyang Li, Li Zhang

Text Generation

Challenge:	Large language models (LLMs) are used to generate a formal representation of a plan in a planning language.
Approach:	They propose a unifying organizational framework based on intermediate representations to unify the inference-time LLM-as-formalizer methodology for classical planning.
Outcome:	The proposed framework subsumes most existing work and proposes new ones that involve syntactically similar but high-resource intermediate languages.

DisComp: A Two-Stage Prompt Optimization Framework Combining Task-Agnostic and Task-Aware Compression (2025.findings-naacl)

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Liu Quancai, Haihui Fan, Jinchao Zhang, Lixiangfang Lixiangfang, Lichuanrong Lichuanrong, Bo Li

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

Challenge:	Extended prompts can lead to substantial computational overhead and increased hardware demands, limiting the scalability and efficiency of large language models.
Approach:	They propose a two-stage prompt compression framework that combines task-agnostic and task-based strategies to efficiently compress prompt length without compromising performance.
Outcome:	The proposed framework outperforms task-agnostic and task-specific compression methods on three benchmark datasets and is up to 6.56 faster at inference compared to the best token-level compression method.

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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

Representation Degeneration Problem in Prompt-based Models for Natural Language Understanding (2024.lrec-main)

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.

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Qingyan Zhao, Ruifang He, Jinpeng Zhang, Chang Liu, Bo Wang

SELECting over Tokens: Curating Pre-training Data at Scale via Token Classification (2026.acl-long)

Challenge:	Prompt-based fine-tuning (PF) models have shown improved performance on few-shot natural language understanding benchmarks.
Approach:	They propose a framework to alleviate anisotropy in the embedding space by aligning with pre-trained language models' training objective.
Outcome:	The proposed method outperforms mainstream methods on many NLU benchmarks.

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Xin Tong, Weidong Zhang, Jiaang Li, Haibin Chen, Shilei Liu, Langming Liu, Kangtao Lv, Yujin Yuan, Wenbo Su, Bo Zheng

DecoupleSearch: Decouple Planning and Search via Hierarchical Reward Modeling (2025.emnlp-main)

Challenge:	Existing pipelines rely on expert-crafted heuristic rules, which lack content-aware, fine-grained noise detection.
Approach:	They propose a framework that reframes data refinement as a highly efficient token classification task.
Outcome:	The proposed framework outperforms existing pipelines on benchmarks and is 2.5x faster at inference.

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Hao Sun, Zile Qiao, Bo Wang, Guoxin Chen, Yingyan Hou, Yong Jiang, Pengjun Xie, Fei Huang, Yan Zhang

CELLO: Causal Evaluation of Large Vision-Language Models (2024.emnlp-main)

Challenge:	Retrieval-Augmented Generation (RAG) systems have emerged as a pivotal methodology for enhancing Large Language Models (LLMs).
Approach:	They propose a framework that decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding.
Outcome:	The proposed framework decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding.

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Meiqi Chen, Bo Peng, Yan Zhang, Chaochao Lu

NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts (2023.findings-acl)

Challenge:	Recent advances in large vision-language models have improved causal reasoning abilities . however, current models struggle with tasks like causal reasoning .
Approach:	They propose a fine-grained and unified definition of causality involving interactions between humans and objects.
Outcome:	The proposed model surpasses traditional commonsense causality by including explicit causal graphs . it also shows that current LVLMs can benefit from a causally inspired prompting strategy .

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Yue Zhang, Bo Zhang, Haochen Jiang, Zhenghua Li, Chen Li, Fei Huang, Min Zhang

FinMaster: A Holistic Benchmark for Full-Pipeline Financial Management with Large Language Models (2026.findings-acl)

Challenge:	Recent studies on Chinese grammatical error correction focus on learning essays.
Approach:	They propose a Chinese grammatical error correction dataset that annotates multiple references for 12,500 sentences from three native domains.
Outcome:	The proposed dataset can be used to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains.

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Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Yujing Zhang, Yilin Xiao, Ruiyu Wang, Bo Li, Xiao Huang, Danny Dongning Sun, Xinrun Wang

mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding (2024.findings-emnlp)

Challenge:	Existing benchmarks lack domain-specific data, realistic workflow-level task design, and standardized workflow- level evaluation.
Approach:	a new benchmark evaluates large language models on financial management workflows . the global financial services market is projected to grow to $37 trillion by 2027 .
Outcome:	a new benchmark for large language models on financial management workflows reveals critical capability gaps . accuracy drops from 90% on basic tasks to 40% on complex scenarios requiring multi-step reasoning . the global financial services market reached $25.8 trillion in 2022 and is projected to grow to $37 trillion by 2027 .

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Anwen Hu, Haiyang Xu, Jiabo Ye, Ming Yan, Liang Zhang, Bo Zhang, Ji Zhang, Qin Jin, Fei Huang, Jingren Zhou

DisCal: Distribution-Aware Calibration for Mathematical Reasoning Under Character-Level Noisy Inputs (2026.acl-long)

Challenge:	Existing Multimodal Large Language Models lack general structure understanding abilities for text-rich document images.
Approach:	They propose to use unified structure learning to boost the performance of MLLMs by encoding structure information into text-rich images.
Outcome:	The proposed model achieves state-of-the-art on 10 visual document understanding benchmarks.

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Bo Zhang, Jiawei Zhang, Cong Gao, Bingxu Han, Minghao Hu, Jun Zhang, Yunbo Cao, Zhunchen Luo, Wen Yao, Guotong Geng, Zhong Wang

Understanding and Improving the Robustness of Terminology Constraints in Neural Machine Translation (2023.acl-long)

Challenge:	Existing methods for calibration of large reasoning models (LRMs) focus on clean inputs, leaving noise unexplored.
Approach:	They propose a confidence calibration framework for character-level noisy inputs that extracts uncertainty signals from both the empirical answer distribution and the model’s predictive distribution and integrates them via a learned calibrator.
Outcome:	Experiments on multiple mathematical reasoning benchmarks show that DisCal outperforms existing calibration methods under noisy inputs, reducing expected calibration error (ECE) by up to 39.21% and improving Area Under the Receiver Operating Characteristic Curve (AUROC) by 31.44%.

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Huaao Zhang, Qiang Wang, Bo Qin, Zelin Shi, Haibo Wang, Ming Chen

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

Challenge:	Existing terminology constraint test sets are blind to this issue due to oversimplified settings . PH methods retain high constraint accuracy but lower translation quality .
Approach:	They propose a method that replaces terminology terms with ordered labels . placeholder methods are better at retaining high constraint accuracy but lower translation quality .
Outcome:	The proposed method achieves high accuracy and translation quality regardless of the number or length of constraints.

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

Revisiting Token Dropping Strategy in Efficient BERT Pretraining (2023.acl-long)

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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Qihuang Zhong, Liang Ding, Juhua Liu, Xuebo Liu, Min Zhang, Bo Du, Dacheng Tao

LLM Reasoning as Trajectories: Step-Specific Representation Geometry and Correctness Signals (2026.acl-long)

Challenge:	Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT.
Approach:	They propose a semantic-consistent learning method to improve token dropping by skipping the computation of a subset of input tokens at several middle layers.
Outcome:	The proposed method achieves consistent and significant performance gains across all tasks and model sizes.

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Lihao Sun, Hang Dong, Bo Qiao, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan

Representation Learning

Challenge:	Existing models generate tokens by updating high-dimensional representations and decoding from them at each timestep.
Approach:	They propose a framework that allows reasoning correction and length control based on derived ideal trajectories.
Outcome:	The proposed model can predict correctness and length control based on ideal trajectories.

LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and Verification (2026.acl-long)

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Penghui Yang, Cunxiao Du, Fengzhuo Zhang, Haonan Wang, Tianyu Pang, Chao Du, Bo An

Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generation (2026.findings-acl)

Challenge:	Large Language Models (LLMs) can process extremely long contexts, requiring efficient inference over extended inputs.
Approach:	They propose a model that uses a constant-sized key-value cache to train long-context models.
Outcome:	Experimental results show that LongSpec achieves 3.26x speedup over strong Flash Attention baselines and 2.34x wall clock time on four math reasoning tasks.

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Linfeng Gao, Qinggang Zhang, Baolong Bi, Bo Zeng, Zheng Yuan, Zerui Chen, Zhimin Wei, Shenghua Liu, Linlong Xu, Longyue Wang, Weihua Luo, Jinsong Su

Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation (2021.acl-long)

Challenge:	Existing approaches to improve contextual faithfulness treat the LLM as a black box, generating responses that are inconsistent with the provided context.
Approach:	They propose a framework for faithful RAG that operates in three stages: (i) fine-grained knowledge pruning to filter irrelevant context, (ii) latent conflict probing to identify hard conflicts in the model’s latent space, and (iv) conflict-aware attention to modulate attention heads toward faithful context integration.
Outcome:	Experiments show that ProbeRAG significantly improves both accuracy and contextual faithfulness.

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Bo Zhang, Xiaoming Zhang, Yun Liu, Lei Cheng, Zhoujun Li

Weed Out, Then Harvest: Dual Low-Rank Adaptation is an Effective Noisy Label Detector for Noise-Robust Learning (2025.findings-acl)

Challenge:	Existing methods require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains.
Approach:	They propose a framework that allows to transfer the knowledge of source domain to the unlabeled target domain without using source data.
Outcome:	The proposed framework matches distributions between a trained source model and a set of target data and achieves superior performance on cross-domain text classification.

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Bo Yuan, Yulin Chen, Yin Zhang

CaMML: Context-Aware Multimodal Learner for Large Models (2024.acl-long)

Challenge:	Experimental results show that PEFT can fine-tune language models without relying on perfectly labeled datasets.
Approach:	They propose a framework that decouples sample selection from model training by introducing clean and noisy LoRA.
Outcome:	The proposed framework decouples sample selection from model training.

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Yixin Chen, Shuai Zhang, Boran Han, Tong He, Bo Li

DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check (2025.acl-long)

Challenge:	a lightweight module for tuning large multimodal models is introduced . CaMML integrates contextual samples into large models, enabling them to make inferences .
Approach:	They introduce a lightweight module for tuning large multimodal models . they have developed two models that have shown exceptional performance .
Outcome:	The proposed model outperforms LLaVA-1.5 on ten widely recognized datasets with a noticeable margin.

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Ziheng Qiao, Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang

Self-Guided Function Calling in Large Language Models via Stepwise Experience Recall (2025.findings-emnlp)

Challenge:	Chinese spelling check (CSC) tasks require that incorrect characters are usually similar to the correct ones in either phonetics or glyph.
Approach:	They propose a plug-and-play decoding intervention with similarity of characters module for Chinese spelling check (CSC) they propose to incorporate phonetic and glyph similarities only during the inference phase.
Outcome:	The proposed method significantly improves Chinese spelling check models on benchmarks and on benchmark datasets.

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Sijia Cui, Aiyao He, Shuai Xu, Hongming Zhang, Yanna Wang, Qingyang Zhang, Yajing Wang, Bo Xu

SParC: Cross-Domain Semantic Parsing in Context (P19-1)

Challenge:	Existing methods for function calling require expert effort and prompt engineering becomes inefficient.
Approach:	They propose a method that performs fine-grained, stepwise retrieval from a continually updated experience pool.
Outcome:	The proposed method achieves an average improvement of 6.1% on easy and 4.7% on hard questions.

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Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, Emily Ji, Shreya Dixit, David Proctor, Sungrok Shim, Jonathan Kraft, Vincent Zhang, Caiming Xiong, Richard Socher, Dragomir Radev

Programming Languages in NLP

Challenge:	Xu et al., 2017): a dataset for cross-domain semantic parsing in context with 4,298 question sequences.
Approach:	They present a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences.
Outcome:	The proposed dataset demonstrates that it has greater semantic diversity and can be generalized to unseen domains due to its cross-domain nature and the unseened databases at test time.

Can Public Large Language Models Help Private Cross-device Federated Learning? (2024.findings-naacl)

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Boxin Wang, Yibo Zhang, Yuan Cao, Bo Li, Hugh McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer

GSID: Generative Semantic Indexing for E-Commerce Product Understanding (2025.emnlp-industry)

Challenge:	Recent studies have shown that public data can be used to improve privacy-utility trade-offs for large and small language models.
Approach:	They propose to use large-scale public data to help differentially private FL training . they propose a distribution matching algorithm with theoretical grounding to sample public data close to private data distribution .
Outcome:	The proposed method is efficient and effective for training private models by taking advantage of public data.

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Haiyang Yang, Qinye Xie, Qingheng Zhang, Chen Li Yu, Huike Zou, Chengbao Lian, Shuguang Han, Fei Huang, Jufeng Chen, Bo Zheng

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

Challenge:	Structured product information is a major bottleneck for the efficiency of e-commerce platforms.
Approach:	They propose a data-driven approach to generate product structured representations using product metadata.
Outcome:	Extensive experiments show that GSID can generate better product representations on real-world e-commerce platforms.

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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.

An Industry Evaluation of Embedding-based Entity Alignment (2020.coling-industry)

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Ziheng Zhang, Hualuo Liu, Jiaoyan Chen, Xi Chen, Bo Liu, YueJia Xiang, Yefeng Zheng

Circuit Complexity Bounds for RoPE-based Transformer Architecture (2025.emnlp-main)

Challenge:	Knowledge graphs (KGs) are increasingly important in various applications such as question answering and search engines.
Approach:	They propose to use a supervised learning environment with unbiased seed mappings for training and validation to evaluate alignment methods in an industrial context.
Outcome:	The proposed methods are evaluated in an industrial context and are compared with DBpedia and Wikidata benchmarks.

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Bo Chen, Xiaoyu Li, Yingyu Liang, Jiangxuan Long, Zhenmei Shi, Zhao Song, Jiahao Zhang

Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference (2024.emnlp-main)

Challenge:	Recent studies provide the circuit complexity bounds to Transformer-like architectures. position embedding has emerged as a crucial technique in modern large language models.
Approach:	They propose to use position embedding to improve Transformer-like architectures by analyzing their circuits and analyzing the results.
Outcome:	The proposed model is able to solve canonical tasks without embedding positional information.

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Jianxing Yu, Shiqi Wang, Han Yin, Zhenlong Sun, Ruobing Xie, Bo Zhang, Yanghui Rao

Representation Learning

Challenge:	a new method to detect clickbait posts on the Web is needed to detect such posts.
Approach:	They propose a method to detect clickbait posts on the Web using latent factors . they use features in multiple modalities to characterize the posts and causal inference to eliminate noise .
Outcome:	The proposed method can detect clickbait posts on popular social media platforms with good generalization ability.

AFMRL: Attribute-Enhanced Fine-Grained Multi-Modal Representation Learning in E-commerce (2026.findings-acl)

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Biao Zhang, Lixin Chen, Bin Zhang, Wang Zongwei, Tong Liu, Bo Zheng

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

Challenge:	Multimodal representation is crucial for E-commerce tasks such as identical product retrieval.
Approach:	They propose an approach which leverages the generative power of Multimodal Large Language Models to extract key attributes from product images and text and enhances representation learning through a two-stage training framework.
Outcome:	The proposed model achieves state-of-the-art on multiple downstream retrieval tasks, validating the effectiveness of harnessing generative models to advance fine-grained representation learning.

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

Data Selection for Multi-turn Dialogue Instruction Tuning (2026.findings-acl)

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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Bo Li, Shikun Zhang, Wei Ye

ReLook: Vision-Grounded RL with a Multimodal LLM Critic for Agentic Web Coding (2026.acl-long)

Challenge:	Instruction-tuned language models often use noisy multi-turn dialogue datasets with topic drift, repetitive chitchat, and mismatched answer formats across turns.
Approach:	They propose a dialogue-level framework that scores whole conversations rather than isolated turns.
Outcome:	The proposed framework outperforms strong single-turn selectors, dialogue-level LLM scorers and heuristic baselines on three multi-turn benchmarks and an in-domain Banking test set.

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Yuhang Li, Chenchen Zhang, Ruilin Lv, Ao Liu, Ken Deng, Yuanxing Zhang, Jiaheng Liu, Bo Zhou

Musical Score Understanding Benchmark: Evaluating Large Language Models’ Comprehension of Complete Musical Scores (2026.acl-long)

Challenge:	Large Language Models (LLMs) excel at algorithmic code generation, but front-end development is lacking in visual fidelity and interaction.
Approach:	They propose an agentic, vision-grounded reinforcement learning framework that closes a loop by invoking a multimodal LLM as a tool.
Outcome:	The proposed framework outperforms baselines in front-end code generation.

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Congren Dai, Yue Yang, Krinos Li, Huichi Zhou, Shijie Liang, Zhang Bo, Enyang Liu, Ge Jin, Hongran An, Haosen Zhang, Peiyuan Jing, KinHei Lee, Zhenxuan Zhang, Xiaobing Li, Maosong Sun

Unsupervised Concept Representation Learning for Length-Varying Text Similarity (2021.naacl-main)

Challenge:	Existing benchmarks for musical score understanding are narrow in scope, focusing on isolated fragments, short excerpts, or multiple-choice formulations, rather than supporting holistic reasoning over entire scores.
Approach:	They propose a benchmark for score-level musical understanding across textual and visual modalities.
Outcome:	The musical score understanding benchmark contains 1,800 question-answer pairs from works by Bach, Beethoven, Chopin, Debussy, and others.

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Xuchao Zhang, Bo Zong, Wei Cheng, Jingchao Ni, Yanchi Liu, Haifeng Chen

Representation Learning

Challenge:	Existing document similarity approaches suffer from the information gap caused by context and vocabulary mismatches when comparing varying-length texts.
Approach:	They propose an unsupervised concept representation learning approach to address this issue . they propose a concept-based document matching method to leverage recognition of local phrase features .
Outcome:	The proposed method achieves a better F1 score than baseline models on real-world data sets.

Learning on Imbalanced Noisy Data via Debiased Sample Selection and LLM-Driven Annotation (2026.findings-acl)

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Bo Yuan, Yulin Chen, Yin Zhang

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

Challenge:	Existing approaches to learning with noisy labels are prone to selection bias and training bias . obtaining large-scale high-quality datasets is expensive and time-consuming in practical scenarios .
Approach:	They propose an imbalanced learning with noisy labels task to let model learn from noisy labels . they first conduct debiased sample selection to better separate clean samples from noisy samples . then they feed selected clean samples to active annotator large language models for re-annotating noisy samples.
Outcome:	The proposed method is superior to existing methods on synthetic and real-world datasets.

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

DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering (2025.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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Rong Cheng, Jinyi Liu, Yan Zheng, Fei Ni, Jiazhen Du, Hangyu Mao, Fuzheng Zhang, Bo Wang, Jianye Hao

Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning (2025.emnlp-main)

Challenge:	Existing approaches to multi-hop question answering struggle to identify and organize dynamic knowledge . et al., 2023; Liu e.t. al. 2023) suggest a dual-process framework for multi-step reasoning .
Approach:	They propose a synergistic dual-process framework that integrates reasoning and retrieval.
Outcome:	The proposed framework improves answer accuracy and coherence even in smaller-scale models.

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Yunhao Gou, Hansi Yang, Zhili Liu, Kai Chen, Yihan Zeng, Lanqing Hong, Zhenguo Li, Qun Liu, Bo Han, James Kwok, Yu Zhang

MMDEND: Dendrite-Inspired Multi-Branch Multi-Compartment Parallel Spiking Neuron for Sequence Modeling (2025.acl-long)

Challenge:	Visual Instruction Tuning (VIT) aims to enhance Multimodal Large Language Models (MLLMs), but its effectiveness is often compromised by corrupted datasets with issues such as hallucinated content and poor OCR quality.
Approach:	They propose a corruption-robust training paradigm that surpasses existing strategies for mitigating the effects of corrupted data.
Outcome:	The proposed training paradigm surpasses existing strategies for mitigating the effects of corrupted data.

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Kexin Wang, Yuhong Chou, Di Shang, Shijie Mei, Jiahong Zhang, Yanbin Huang, Man Yao, Bo Xu, Guoqi Li

The Rise of Darkness: Safety-Utility Trade-Offs in Role-Playing Dialogue Agents (2025.findings-acl)

Challenge:	Vanilla spiking neurons are simplified from complex biological neurons with dendrites, soma, and synapses into single somatic compartments.
Approach:	They propose a multi-branch, multi-compartment parallel spiking dendritic neuron with a proportion-adjustable multi-branched structure that enables long-term temporal dependencies.
Outcome:	The proposed model achieves better long-sequence modeling capability with fewer parameters and lower energy consumption.

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Yihong Tang, Kehai Chen, Xuefeng Bai, Zheng-Yu Niu, Bo Wang, Jie Liu, Min Zhang

Read As Human: Compressing Context via Parallelizable Close Reading and Skimming (2026.acl-long)

Challenge:	Large Language Models (LLMs) demonstrate their utility in character simulations, but they pose a risk of generating unsafe content.
Approach:	They propose a method which dynamically adjusts safety-utility preferences based on the degree of risk coupling and guides the model to generate responses biased toward utility or safety.
Outcome:	The proposed method improves safety metrics while maintaining utility.

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Jiwei Tang, Shilei Liu, Zhicheng Zhang, Qingsong Lv, Runsong Zhao, Tingwei Lu, Langming Liu, Haibin Chen, Yujin Yuan, Hai-Tao Zheng, Wenbo Su, Bo Zheng

Challenge:	Existing task-aware methods require loading the entire input sequence at once for compression, which suffer from computational inefficiency.
Approach:	They propose a framework that adopts an adaptive hybrid reading strategy to reduce computational inefficiency and redundant information in long-context scenarios.
Outcome:	Experiments show that RAM outperforms baselines on multiple question answering and summarization benchmarks while delivering up to a 12x speedup on long inputs.

Beyond Query Memorization: Large Language Model Routing with Query Decomposition and Historical Matching (2026.acl-long)

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Bo Lv, Jingbo Sun, Jianwei Lv, Chen Tang, Shaojie Zhang, Nayu Liu, Guoxin Yu, Zihao Li, Qichao Zhang, Dongbin Zhao, Ping Luo, Yue Yu

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

Challenge:	Existing routing methods rely on direct mapping from queries to models based on surface-level features, leading to poor generalizability on out-of-distribution data.
Approach:	They propose a new routing framework that recasts the routing task as a matching process of sifting similar queries from historical logs.
Outcome:	The proposed framework improves matching accuracy while lowering inference costs . it decouples linguistic surface forms from task-intrinsic requirements .

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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

Towards Interpretable Tabular Reasoning: Enhancing LLM Reasoning on Tabular Data with Pre-Constructed Logic Graph (2026.acl-long)

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.

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Lirong Gao, Zewei Yu, Zhongrui Yin, Qi Zhang, Yuke Zhu, Bo Zheng, Haobo Wang, Junbo Zhao, Gang Chen, Sheng Guo

MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models (2026.acl-long)

Challenge:	Tabular data is used in fields such as finance and healthcare due to its heterogeneity and complexity.
Approach:	They propose a Logic-Graph-Enhanced LLM Reasoning framework that integrates the strengths of tree-based models and LLMs to improve their interpretability.
Outcome:	The proposed framework outperforms tree-based models and state-of-the-art LLMs on tabular prediction tasks, achieving superior accuracy and interpretability.

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Jie Cao, Tianwei Lin, Bo Yuan, Rolan Yan, Hongyang He, Wenqiao Zhang, Juncheng Li, Dongping Zhang, Siliang Tang, Yueting Zhuang

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

Challenge:	Existing methods for parameter-efficient fine-tuning (PEFT) are limited by computational costs and performance degradation.
Approach:	They propose a method that integrates Low-Rank Adaptation and Mixture-of-Experts (MoE) they propose combining expert load imbalance and representation collapse to improve LLM performance .
Outcome:	The proposed method outperforms homogeneous MoE-LoRA architectures in performance and parameter efficiency.

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

Domain-aware and Co-adaptive Feature Transformation for Domain Adaption Few-shot Relation Extraction (2024.lrec-main)

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

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Yijun Liu, Feifei Dai, Xiaoyan Gu, Minghui Zhai, Bo Li, Meiou Zhang

MegaPairs: Massive Data Synthesis for Universal Multimodal Retrieval (2025.acl-long)

Challenge:	Existing approaches to relation extraction focus on the source domain, which makes it difficult to accurately transfer useful knowledge to the target domain.
Approach:	They propose a domain-aware and co-adaptive feature transformation approach to address these issues by leveraging the target domain distribution features to guide the domain-based feature transformations.
Outcome:	The proposed method outperforms existing models and achieves state-of-the-art performance on a benchmark dataset.

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Junjie Zhou, Yongping Xiong, Zheng Liu, Ze Liu, Shitao Xiao, Yueze Wang, Bo Zhao, Chen Jason Zhang, Defu Lian

CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)

Challenge:	despite the growing demand for multimodal retrieval, there is a lack of training data.
Approach:	They propose a data synthesis method that leverages vision language models and open-domain images to generate high-quality data.
Outcome:	The proposed method outperforms baseline models on 70 more datasets and can scale up.

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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan

Challenge:	Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages .
Approach:	They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models .
Outcome:	The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English .

M³GQA: A Multi-Entity Multi-Hop Multi-Setting Graph Question Answering Benchmark (2025.acl-long)

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Boci Peng, Yongchao Liu, Xiaohe Bo, Jiaxin Guo, Yun Zhu, Xuanbo Fan, Chuntao Hong, Yan Zhang

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

Challenge:	GraphRAG systems have achieved remarkable progress in enhancing performance and reliability of large language models.
Approach:	They propose a GraphRAG benchmark focusing on multi-entity queries with six settings for comprehensive evaluation.
Outcome:	The proposed method can construct diverse data with semantically correct ground-truth reasoning paths.

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

PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models (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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Haoyu Zheng, Yun Zhu, Yuqian Yuan, Bo Yuan, Wenqiao Zhang, Siliang Tang, Jun Xiao

RealMedDial: A Real Telemedical Dialogue Dataset Collected from Online Chinese Short-Video Clips (2022.coling-1)

Challenge:	Large Language Models lack the capacity to formulate global strategies due to latency and availability constraints.
Approach:	They propose a framework to internalize the strategic oversight of large models into intrinsic Latent Guidance by synthesizing a query-conditioned Latent Guide.
Outcome:	The proposed framework outperforms strong baselines on mathematical and coding benchmarks with negligible inference latency.

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Bo Xu, Hongtong Zhang, Jian Wang, Xiaokun Zhang, Dezhi Hao, Linlin Zong, Hongfei Lin, Fenglong Ma

EDDA: An Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection (2024.lrec-main)

Challenge:	Existing medical dialogue systems are limited by the lack of corpora and data from real scenarios.
Approach:	They construct a Chinese medical dialogue dataset based on real medical consultations.
Outcome:	The proposed dataset is applicable to a wide range of NLP tasks with respect to medical dialogue.

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Daijun Ding, Li Dong, Zhichao Huang, Guangning Xu, Xu Huang, Bo Liu, Liwen Jing, Bowen Zhang

Towards Provably Secure Generative AI: Reliable Consensus Sampling (2026.findings-acl)

Challenge:	Existing methods for enhancing text or data are limited by lack of logical connections between generated texts and training data.
Approach:	They propose an encoder-decoder data augmentation framework that combines large language models and chain-of-thought prompting to summarize texts into target-specific if-then rationales, establishing logical relationships.
Outcome:	The proposed framework significantly improves over state-of-the-art methods on benchmark datasets while enabling interpretable rationale-based learning.

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Yu Cui, Hang Fu, Sicheng Pan, Zhuoyu Sun, Yifei Liu, Yuhong Nie, Bo Ran, Baohan Huang, Xufeng Zhang, Haibin Zhang, Cong Zuo, Licheng Wang

Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)

Challenge:	Existing research on generative AI security is driven by mutually reinforcing attack and defense methodologies grounded in empirical experience.
Approach:	They propose a new algorithm that uses a random sampling algorithm to control risk.
Outcome:	The proposed algorithm improves robustness and utility while maintaining latency comparable to existing algorithms.

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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang

Challenge:	Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions?
Approach:	They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values.
Outcome:	The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure.

Instruction Data Selection via Answer Divergence (2026.acl-long)

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Bo Li, Mingda Wang, Shikun Zhang, Wei Ye

Challenge:	Existing methods for instruction tuning use data-centric methods, but they do not explicitly reflect what a particular base model is missing.
Approach:	They propose a method for instruction tuning that uses geometric structure of multi-sample outputs to select instruction data.
Outcome:	The proposed approach outperforms strong selectors on six benchmarks spanning reasoning, knowledge, and coding.

MPL: Multiple Programming Languages with Large Language Models for Information Extraction (2025.findings-acl)

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Bo Li, Gexiang Fang, Wei Ye, Zhenghua Xu, Jinglei Zhang, Hao Cheng, Shikun Zhang

EvoNarrator: Modeling Scientific Evolution for Feasible Hypothesis Generation (2026.acl-long)

Challenge:	Existing research focuses on Python for code-style simulation, overlooking the potential of other widely-used PLs during the supervised fine-tuning phase.
Approach:	They propose a framework that incorporates programming languages into IE tasks . they introduce function-prompt with virtual running to simulate code-style inputs .
Outcome:	The proposed framework exploits the potential of different programming languages during the supervised fine-tuning phase.

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Xiaoying Le, Pengfei Qian, Yuanzhao Zhai, Xu Zhang, Qian Liu, Feng Dawei, Bo Ding

Challenge:	Scientific discovery evolution does not occur ex nihilo but is characterized by structural deepening and reconfiguration of existing functionalities.
Approach:	They propose a framework for hypothesis generation based on evolutionary narratives . they extract structured P-M-L-F quadruples from citation networks and introduce a mechanism to assess their semantic compatibility.
Outcome:	The proposed framework reduces logical disconnects by evaluating its semantic compatibility.

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

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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

Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient (2025.acl-long)

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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Yuan Gao, Zujing Liu, Weizhong Zhang, Bo Du, Gui-Song Xia

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

Challenge:	Recent pruning methods rely on heuristically hand-crafted metrics, leading to suboptimal performance.
Approach:	They propose a method that optimizes pruning masks by minimizing back-propagation . they learn an underlying Bernoulli distribution to sample binary pruning mask samples .
Outcome:	The proposed method is able to support global and heterogeneous pruning without back-propagation.

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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.

Sensitivity-LoRA : Low-Load Sensitivity-Based Fine-Tuning for Large Language Models (2025.findings-emnlp)

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Hao Zhang, Bo Huang, Zhenjia Li, Xi Xiao, Hui Yi Leong, Zumeng Zhang, Xinwei Long, Tianyang Wang, Hao Xu

ELAD: Explanation-Guided Large Language Models Active Distillation (2024.findings-acl)

Challenge:	Low-Rank Adaptation (LoRA) is a promising approach to adapting LLMs to specialized tasks . existing rank allocation techniques remain computationally inefficient and unstable .
Approach:	They propose a low-rank adapted model that approximates model weight updates using low-ranked decomposition.
Outcome:	The proposed method is limited by its uniform rank allocation to each incremental matrix . it leverages the second-order derivatives of the loss function to capture weight sensitivity .

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Yifei Zhang, Bo Pan, Chen Ling, Yuntong Hu, Liang Zhao

AIGuard: A Benchmark and Lightweight Detection for E-commerce AIGC Risks (2025.findings-acl)

Challenge:	Large Language Models (LLMs) are hindered by their memory inefficiency, computational demands, and the high costs of API inferences.
Approach:	They propose an Explanation-Guided LLMs Active Distillation framework that employs an active learning strategy to optimize the balance between annotation costs and model performance.
Outcome:	The proposed framework significantly improves the efficiency of LLMs knowledge distillation.

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Wenhua Zhang, Weicheng Li, Xuanrong Rao, Lixin Zou, Xiangyang Luo, Chubin Zhuang, Yongjie Hong, Zhen Qin, Hengyu Chang, Chenliang Li, Bo Zheng

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning (2025.emnlp-main)

Challenge:	Existing detection methods lack real-world scenarios and corresponding risk datasets . current MLLMs lack knowledge and have limited capability to detect the risk of AIGC content.
Approach:	They propose a benchmark for AIGC risk detection in real-world e-commerce . it includes 253,420 image-text pairs across four critical categories .
Outcome:	The proposed method achieves 9.68% higher recall than leading multimodal models while using only 25% of training resources.

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Le Zhang, Bo Wang, Xipeng Qiu, Siva Reddy, Aishwarya Agrawal

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

Challenge:	REARANK is a large language model-based listwise reasoning reranking agent . it explicitly reasons be- fore reranked results, significantly improving performance and interpretability.
Approach:	They propose a large language model-based listwise reasoning reranking agent that explicitly reasons be- fore reranked lists.
Outcome:	The proposed agent outperforms GPT-4 on reasoning-intensive benchmarks and surpasses GPL-4 on BRIGHT benchmarks.

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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 .

Igniting Creative Writing in Small Language Models: LLM-as-a-Judge versus Multi-Agent Refined Rewards (2025.emnlp-main)

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Xiaolong Wei, Bo Lu, Xingyu Zhang, Zhejun Zhao, Dongdong Shen, Long Xia, Dawei Yin

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

Challenge:	Existing methods for enhancing Large Language Models (LLMs) struggle with novelty and Reinforcement Learning from human feedback (RLHF) is costly.
Approach:	They propose to use a Reward Model (RM) and a principle-guided LLM-as-a-Judge to enhance creative output over baselines.
Outcome:	The proposed approach significantly enhances creative output over baselines, but the principle-guided LLM-as-a-Judge yields superior generation quality.

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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.

CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases (2025.naacl-long)

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Xiangyan Liu, Bo Lan, Zhiyuan Hu, Yang Liu, Zhicheng Zhang, Fei Wang, Michael Qizhe Shieh, Wenmeng Zhou

CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation (2023.acl-long)

Challenge:	Large Language Models excel in stand-alone code tasks but struggle with handling entire code repositories.
Approach:	They propose a system that integrates LLM agents with graph database interfaces extracted from code repositories.
Outcome:	The proposed system integrates LLM agents with graph database interfaces extracted from code repositories.

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Jinfeng Zhou, Chujie Zheng, Bo Wang, Zheng Zhang, Minlie Huang

GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)

Challenge:	Existing empathetic dialogue models only consider the affective aspect of empathy, which limits the capability of emotional response generation.
Approach:	They propose a model that aligns the user's cognition and affection at both the coarse-grained and fine-grounded levels and then automatically and manually evaluates the model.
Outcome:	The proposed model outperforms state-of-the-art models and generates more empathetic and informative responses.

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Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen

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

Challenge:	GigaSpeech 2 is a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages.
Approach:	They propose a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages and an automated pipeline for data crawling, transcription, and label refinement.
Outcome:	The proposed corpus reduces the word error rate for Thai, Indonesian, and Vietnamese on a realistic YouTube test set by 25% to 40% compared to Whisper large-v3.

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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

Self-Supervised Sentence Polishing by Adding Engaging Modifiers (2023.acl-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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Zhexin Zhang, Jian Guan, Xin Cui, Yu Ran, Bo Liu, Minlie Huang

SURVEYFORGE : On the Outline Heuristics, Memory-Driven Generation, and Multi-dimensional Evaluation for Automated Survey Writing (2025.acl-long)

Challenge:	a typical way to polish sentences is to add engaging modifiers, which enhance the meaning of the sentence.
Approach:	They propose a task that requires polishing sentences while maintaining fluency . they remove engaging modifiers from public resources and fine-tune LongLM to reconstruct original sentences from corrupted ones.
Outcome:	The proposed model generates more engaging sentences with suitable modifiers than strong baselines while keeping fluency.

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Xiangchao Yan, Shiyang Feng, Jiakang Yuan, Renqiu Xia, Bin Wang, Lei Bai, Bo Zhang

SMEC:Rethinking Matryoshka Representation Learning for Retrieval Embedding Compression (2025.emnlp-main)

Challenge:	SURVEYFORGE automates survey paper writing, but quality gap between LLM-generated and human-written surveys remains significant.
Approach:	They propose a survey tool that automatically generates and refines human-written surveys.
Outcome:	Experiments show that SURVEYFORGE outperforms previous work such as AutoSurvey in outline quality and content quality.

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Biao Zhang, Lixin Chen, Tong Liu, Bo Zheng

Graph Enhanced Dual Attention Network for Document-Level Relation Extraction (2020.coling-main)

Challenge:	Large language models generate high-dimensional embeddings that capture rich semantic and syntactic information.
Approach:	They propose a training framework to reduce dimensionality and complexity of large language models.
Outcome:	Experiments on image, text, and multimodal datasets show that the proposed training framework reduces dimensionality while maintaining performance.

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Bo Li, Wei Ye, Zhonghao Sheng, Rui Xie, Xiangyu Xi, Shikun Zhang

Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions (P18-2)

Challenge:	Document-level relation extraction requires inter-sentence reasoning capabilities to capture local and global contextual information for multiple relation facts.
Approach:	They propose to characterize the interaction between sentences and potential relation instances via a Graph Enhanced Dual Attention network (GEDA) . they also propose a simple yet effective regularizer based on the natural duality of the S2R and R2S attentions, whose weights are also supervised by the supporting evidence of relation instances during training.
Outcome:	The proposed model achieves competitive performance on an existing large-scale dataset while the predictions can be interpretable and easily observed.

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Dongyu Zhang, Hongfei Lin, Liang Yang, Shaowu Zhang, Bo Xu

Sentiment Analysis

Challenge:	a corpus of 5,605 manually annotated sentences in Chinese is described . emotion is an abstract and vague conception, which is often described by metaphor .
Approach:	They propose to construct a corpus of metaphors annotated with emotion in Chinese . they use an annotation scheme to include linguistic metaphors, emotional categories and intensity .
Outcome:	The proposed corpus contains 5,605 manually annotated sentences in Chinese . the authors show that the corpus is large enough to analyze emotions .

Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models (2024.acl-long)

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Xudong Lu, Qi Liu, Yuhui Xu, Aojun Zhou, Siyuan Huang, Bo Zhang, Junchi Yan, Hongsheng Li

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

Challenge:	Mixture-of-Experts (MoE) LLMs achieve higher performance with fewer active parameters, but are still difficult to deploy due to their immense parameter sizes.
Approach:	They propose expert-level sparsification techniques to enhance the deployment efficiency of large language models by introducing plug-and-play expert pruning and skipping techniques.
Outcome:	The proposed methods reduce model sizes and increase inference speed while maintaining satisfactory performance across a wide range of tasks.

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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

Text Detoxification: Data Efficiency, Semantic Preservation and Model Generalization (2025.emnlp-main)

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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Jing Yu, Yibo Zhao, Jiapeng Zhu, Wenming Shao, Bo Pang, Zhao Zhang, Xiang Li

MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus (2026.findings-acl)

Challenge:	Existing methods for detoxification of text often rely on manually annotated data . xiangli: "detoxification of texts is a powerful way to remove toxic content"
Approach:	They propose a reinforcement learning framework that optimizes detoxification and semantic preservation without annotating large amounts of data.
Outcome:	The proposed method overcomes major limitations and surpasses humanannotated references across multiple benchmarks.

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Yexing Du, Kaiyuan Liu, Bihe Zhang, Youcheng Pan, Bo Yang, Liangyu Huo, Xiyuan Zhang, Jian Xie, Daojing He, Yang Xiang, Ming Liu, Bing Qin

URG: A Unified Ranking and Generation Method for Ensembling Language Models (2024.findings-acl)

Challenge:	Multimodal Large Language Models (MLLMs) have advanced Chinese Classical Studies (CCS) but the audio dimension of CCS remains underexplored due to a lack of high-quality, domain-specific audio corpora.
Approach:	They propose a 119-hour audio corpus comprising 22,000 audio samples to bridge this gap . it encompasses a diverse range of literary genres across six tasks .
Outcome:	The proposed corpus encompasses a diverse range of literary genres across six tasks: Automatic Speech Recognition (ASR), Speech-to-Text Translation (S2TT), Speech Emotion Captioning (SEC), Spoken Question Answering ( SQA), Speech Understanding (SU), and Speech Reasoning (SR).

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Bo Lv, Chen Tang, Yanan Zhang, Xin Liu, Ping Luo, Yue Yu

WYWEB: A NLP Evaluation Benchmark For Classical Chinese (2023.findings-acl)

Challenge:	Existing approaches to rank and generate large language models have limited performance due to time-intensive nature of ranking process and lack of error propagation.
Approach:	They propose a framework that jointly ranks the outputs of Large Language Models and generates fine-grained fusion results.
Outcome:	The proposed framework achieves state-of-the-art (SOTA) performance on ranking and generation tasks.

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Bo Zhou, Qianglong Chen, Tianyu Wang, Xiaomi Zhong, Yin Zhang

Challenge:	Existing benchmarks for classical Chinese are inadequate to evaluate performance of different NLP models.
Approach:	They propose an evaluation benchmark for classical Chinese NLP, which evaluates existing models.
Outcome:	The proposed benchmark evaluates the performance of existing models in classical Chinese.

DAGCN: Distance-based and Aspect-oriented Graph Convolutional Network for Aspect-based Sentiment Analysis (2024.findings-naacl)

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Zhihao Wang, Bo Zhang, Ru Yang, Chang Guo, Maozhen Li

Sentiment Analysis

Challenge:	Recent advances in sentiment analysis tend to interference from local factors such as irrelevant words and edges, hindering the precise identification of opinion words.
Approach:	They propose a distance-based syntactic weight and Aspect-Fusion Attention to solve this problem.
Outcome:	The proposed model outperforms state-of-the-art models on three public datasets and verify its effectiveness.

Towards Verifiable Text Generation with Evolving Memory and Self-Reflection (2024.emnlp-main)

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Hao Sun, Hengyi Cai, Bo Wang, Yingyan Hou, Xiaochi Wei, Shuaiqiang Wang, Yan Zhang, Dawei Yin

Hierarchy Response Learning for Neural Conversation Generation (D19-1)

Challenge:	Large language models (LLMs) often produce factually incorrect information, also known as hallucination.
Approach:	They propose a framework for verifiable text generation with evolving memory and self-reflection that incorporates long-term memory to retain documents and recent documents.
Outcome:	The proposed framework outperforms baselines on five datasets across three knowledge-intensive tasks.

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Bo Zhang, Xiaoming Zhang

MARIO-0.5B: A Multi-Agent Lightweight Model for Real-Time Open Information Extraction in Low-Resource Settings (2025.findings-emnlp)

Challenge:	Neural conversation generation models can't perceive and express the intention effectively, causing dull and generic responses.
Approach:	They propose a hierarchical response generation model to capture conversation intention . they propose an expression reconstruction model and an expression attention model .
Outcome:	The proposed model can generate the responses with more appropriate content and expression.

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Donghai Zhang, SHuangtao Yang, Dong Xiaozheng, Wei Song, Bo Fu

MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction (2022.naacl-main)

Challenge:	Large language models have shown remarkable capabilities in open information extraction, but their resource requirements often restrict their deployment in resource-constrained industrial settings.
Approach:	They introduce an ultra-lightweight large language model trained on instruction-based samples in Chinese, English, Korean, and Russian.
Outcome:	The proposed model outperforms large-scale models with up to 70B parameters, reducing computational resources by 140x and delivering 11x faster response times.

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Yue Zhang, Zhenghua Li, Zuyi Bao, Jiacheng Li, Bo Zhang, Chen Li, Fei Huang, Min Zhang

RENN: A Rule Embedding Enhanced Neural Network Framework for Temporal Knowledge Graph Completion (2024.lrec-main)

Challenge:	Using a multi-reference multi-source evaluation dataset, Chinese grammatical error correction (CGEC) is relatively scarce.
Approach:	They propose a multi-reference multi-source evaluation dataset for Chinese grammar error correction . the dataset contains 7,063 sentences written by Chinese-as-a-Second-Language learners .
Outcome:	The proposed dataset can be used to evaluate Chinese grammar errors in Chinese.

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Linlin Zong, Zhenrong Xie, Chi Ma, Xinyue Liu, Xianchao Zhang, Bo Xu

FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning (2021.findings-emnlp)

Challenge:	Existing methods for temporal knowledge graph embedding do not account for structural dependencies between relations.
Approach:	They propose a framework that enhances temporal knowledge graph completion through rule embedding.
Outcome:	The proposed framework improves temporal knowledge graph completion through rule embedding.

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Xu Wang, Hainan Zhang, Shuai Zhao, Yanyan Zou, Hongshen Chen, Zhuoye Ding, Bo Cheng, Yanyan Lan

LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models (2025.findings-naacl)

Challenge:	Existing neural dialogue models only capture syntactic and semantic information, but fail to model the logical consistency between the dialogue history and the generated response.
Approach:	They propose a fine-grained comparison model to capture syntactic and semantic information and then compare each candidate's representation with the whole history to obtain a history consistency representation.
Outcome:	The proposed model obtains higher ranking scores than baseline models on two public dialogue datasets.

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Kaichen Zhang, Bo Li, Peiyuan Zhang, Fanyi Pu, Joshua Adrian Cahyono, Kairui Hu, Shuai Liu, Yuanhan Zhang, Jingkang Yang, Chunyuan Li, Ziwei Liu

How to Make LMs Strong Node Classifiers? (2026.findings-eacl)

Challenge:	Current large foundational models have demonstrated transformative capabilities, approaching or surpassing human-level performances in many tasks.
Approach:	They propose a unified and standardized multimodal benchmark framework with over 50 tasks and more than 10 models to promote transparent and reproducible evaluations.
Outcome:	The proposed framework has 50 tasks and more than 10 models to promote transparent and reproducible evaluations.

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Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong

Exploring Label Hierarchy in a Generative Way for Hierarchical Text Classification (2022.coling-1)

Challenge:	Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs).
Approach:	They propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the art (SOTA) GNNs on node classification tasks without requiring any architectural modifications.
Outcome:	The proposed approach outperforms existing GNNs on node classification tasks and is open-source upon publication.

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Wei Huang, Chen Liu, Bo Xiao, Yihua Zhao, Zhaoming Pan, Zhimin Zhang, Xinyun Yang, Guiquan Liu

Text Classification

Challenge:	Existing methods for hierarchical text classification are lacking in the field of natural language processing.
Approach:	They propose a hierarchy-aware T5 model with path-adaptive attention mechanism to exploit hierarchical dependency across different levels.
Outcome:	The proposed model outperforms state-of-the-art models especially in Macro-F1 and low Macro.

Geoparsing: Diagram Parsing for Plane and Solid Geometry with a Unified Formal Language (2026.findings-acl)

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Peijie Wang, Ming-Liang Zhang, Jun Cao, Chao Deng, Dekang Ran, Pi Bu, Hongda Sun, Xuan Zhang, Yingyao Wang, Jun Song, Bo Zheng, Fei Yin, Cheng-Lin Liu

A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language Models (2024.emnlp-main)

Challenge:	Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across various vision reasoning tasks.
Approach:	They propose a unified formal language that integrates plane and solid geometry, comprehensively covering geometric structures and semantic relations.
Outcome:	The proposed language achieves state-of-the-art parsing performance and significantly boosts MLLMs’ capabilities for downstream geometry reasoning tasks.

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Houquan Zhou, Zhenghua Li, Bo Zhang, Chen Li, Shaopeng Lai, Ji Zhang, Fei Huang, Min Zhang

Self-adaptive Dataset Construction for Real-World Multimodal Safety Scenarios (2025.findings-emnlp)

Challenge:	Using an LLM for Chinese spelling correction tasks is completely different from previous approaches . given a Chinese character, there may exist many others with the same or similar pronunciations, or with similar shapes.
Approach:	They propose a training-free prompt-free approach to leverage large language models for Chinese spelling correction task.
Outcome:	The proposed model significantly improves performance on five public datasets, enabling them to compete with state-of-the-art domain-general CSC models.

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Jingen Qu, Lijun Li, Bo Zhang, Yichen Yan, Jing Shao

Making MLLMs Blind: Adversarial Smuggling Attacks in MLLM Content Moderation (2026.findings-acl)

Challenge:	Existing dataset construction methods fail to cover the complexity of multimodal safety scenarios . lack of a unified evaluation metric makes them unproven .
Approach:	They propose a risk-oriented image-oriented self-adaptive dataset construction method for RMS . they automatically generate an RMS dataset comprising 35,610 image–text pairs with guidance responses .
Outcome:	The proposed method automatically generates an RMS dataset comprising 35,610 image–text pairs with guidance responses.

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Zhiheng Li, Zongyang Ma, Yuntong Pan, Ziqi Zhang, Xiaolei Lv, Bo Li, Jun Gao, Jianing Zhang, Chunfeng Yuan, Bing Li, Weiming Hu

IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation (2026.acl-long)

Challenge:	Multimodal Large Language Models (MLLMs) are increasingly being deployed as content moderators . however, they exploit the Human-AI capability gap and create adversarial environments . smuggling attacks exploit the human-AI gap and exploit the vulnerability .
Approach:	They construct a benchmark to evaluate the vulnerability of MLLMs as content moderators . they identify three root causes: limited capabilities of vision encoders, robustness gap in OCR .
Outcome:	The proposed model exploits the Human-AI capability gap and is vulnerable to smuggling attacks.

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Yinghao Tang, Xueding Liu, Boyuan Zhang, Tingfeng Lan, Yupeng Xie, Jiale Lao, Yiyao Wang, Haoxuan Li, Tingting Gao, Bo Pan, Luoxuan Weng, Xiuqi Huang, Minfeng Zhu, Yingchaojie Feng, Yuyu Luo, Wei Chen

AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding (2020.findings-emnlp)

Challenge:	Generated infographics may appear correct at first glance but contain easily overlooked issues, such as distorted data encoding or incorrect textual content.
Approach:	They propose to evaluate reliability of text-to-infographic generation using IGenBench . they employ multimodal large language models to verify each question .
Outcome:	The proposed framework decomposes reliability verification into atomic yes/no questions based on a taxonomy of 10 question types.

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Guanglin Niu, Bo Li, Yongfei Zhang, Shiliang Pu, Jingyang Li

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

Challenge:	Existing knowledge graphs are incomplete whether they are constructed manually or automatically, limiting the effectiveness when exploited for downstream applications.
Approach:	They propose a KGE framework with an automatic type embedding mechanism which can be easily integrated into any existing KGE model.
Outcome:	The proposed model can model and infer all the relation patterns and complex relations compared to state-of-the-art models on four datasets.

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

GRAPHIA: Harnessing Social Graph Data to Enhance LLM-Based Social Simulation (2026.acl-long)

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

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Jiarui Ji, Zehua Zhang, Zhewei Wei, Bin Tong, Guan Wang, Bo Zheng

RESEMO: A Benchmark Chinese Dataset for Studying Responsive Emotion from Social Media Content (2024.findings-acl)

Challenge:	Social graphs provide high-quality supervision signals that encode local interactions and global network structure, yet they remain underutilized for LLM training.
Approach:	They propose a general LLM-based social graph simulation framework that leverages graph data as supervision for LLM training.
Outcome:	The proposed framework improves micro-level alignment by 6.1% on three real-world networks compared to the strongest baseline.

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Bo Hu, Meng Zhang, Chenfei Xie, Yuanhe Tian, Yan Song, Zhendong Mao

Class Lifelong Learning for Intent Detection via Structure Consolidation Networks (2023.findings-acl)

Challenge:	Existing studies on social media text processing do not focus on responsive emotion analysis.
Approach:	They propose a Chinese dataset named ResEmo for responsive emotion analysis, including 3813 posts with 68,781 comments collected from Weibo, the largest social media platform in China.
Outcome:	The proposed dataset includes 3813 posts with 68,781 comments collected from weibo, the largest social media platform in China.

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Qingbin Liu, Yanchao Hao, Xiaolong Liu, Bo Li, Dianbo Sui, Shizhu He, Kang Liu, Jun Zhao, Xi Chen, Ningyu Zhang, Jiaoyan Chen

Sentiment Analysis

Challenge:	Existing intent detection models can only handle predefined intent classes in the offline environment.
Approach:	They propose a method that continually learns new intent classes from new data . structure-based retrospection and contrastive knowledge distillation are used to solve these problems .
Outcome:	The proposed method outperforms existing models on three benchmarks.

FlowSearch: Advancing Deep Research with Dynamic Structured Knowledge Flow (2026.acl-long)

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Yusong Hu, Runmin Ma, Yue Fan, Jinxin Shi, Zongsheng Cao, Yuhao Zhou, Jiakang Yuan, Shuaiyu Zhang, Shiyang Feng, Xiangchao Yan, Shufei Zhang, Wenlong Zhang, Lei Bai, Bo Zhang

SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser (2022.emnlp-main)

Challenge:	FlowSearch is a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning.
Approach:	They propose a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning.
Outcome:	The proposed framework achieves competitive performance on GAIA, HLE, GPQA and TRQA benchmarks and is available to download.

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Yue Zhang, Bo Zhang, Zhenghua Li, Zuyi Bao, Chen Li, Min Zhang

RAVR: Reference-Answer-guided Variational Reasoning for Large Language Models (2026.findings-acl)

Challenge:	Existing approaches to grammatical error correction are unreliable when processing ungrammatically . a new approach is proposed that incorporates dependency syntactic information into the encoder part of GEC models.
Approach:	They propose a syntax-enhanced grammatical error correction approach called SynGEC that incorporates dependency syntactic information into the encoder part of GEC models.
Outcome:	The proposed approach outperforms strong baselines and achieves competitive performance on mainstream English and Chinese GEC datasets.

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Tianqianjin Lin, Xi Zhao, Xingyao Zhang, Rujiao Long, Yi Xu, Zhuoren Jiang, Wenbo Su, Bo Zheng

Temporal Evidence Chain for Temporal Knowledge Graph Question Answering with Large Language Models (2026.acl-long)

Challenge:	Experiments show that reinforcement learning (RL) can refine the reasoning abilities of large language models (LLMs) but requires a key prerequisite: the model must already be able to generate high-utility reasoning paths with non-negligible probability.
Approach:	They propose a framework that uses answer-conditioned reasoning as a variational surrogate for question-only reasoning.
Outcome:	Experiments on 11 benchmarks and 3 models show that RAVR reduces hesitation, strengthens conclusion consolidation, and promotes problem-specific strategies in reasoning.

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Shihao Liu, Xiaofei Zhou, Bo Wang, Geyuan Zhang

FinMRAGBench: A Realistic and Complex Benchmark for Multi-Modal RAG in Financial Document Analysis (2026.findings-acl)

Challenge:	Temporal Knowledge Graph Question Answering (TKGQA) aims to answer temporal questions using knowledge from Temporal knowledge graphs.
Approach:	They propose a framework to construct temporal evidence chains for LLM reasoning using Temporal Knowledge Graphs.
Outcome:	TECQA outperforms existing methods on MultiTQ and CronQuestions.

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Shouqing Yang, Qi Zhang, Yuhang Yang, Ruikang Xu, Yuwei Hou, Zhulin Jia, Lirong Gao, Haobo Wang, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Gang Chen

Don’t Be Misled by Style: A Style-Adaptive Reranker for Capturing Effective Knowledge in Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Existing benchmarks for realistic financial analysis fail to capture realistic financial situations involving cross-document retrieval, multi-page evidence integration, and diverse analytical tasks.
Approach:	They propose a multi-modal financial RAG benchmark that evaluates large language models in realistic financial analysis settings.
Outcome:	The proposed framework achieves the strongest overall performance across all models.

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Ruwen Zhang, Bo Liu, Zhang Sheng Xiang, Yida Chen, Hantao Zhao, Ding Ding, Jiahui Jin, Jiuxin Cao

GRAG: Graph Retrieval-Augmented Generation (2025.findings-naacl)

Challenge:	Existing rerankers are mainly trained on well-edited texts, but stylistic features can be misled by reranked models.
Approach:	They propose a style-augmented multi-task framework that prioritizes effective knowledge over stylistic perturbations by using an LLM to derive passage-level supervision on whether a passage helps or harms answer correctness.
Outcome:	Extensive experiments show that SARK improves generation performance across multiple LLMs under mixed-style conditions.

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Yuntong Hu, Zhihan Lei, Zheng Zhang, Bo Pan, Chen Ling, Liang Zhao

Structured Data in NLP

Challenge:	Naive Retrieval-Augmented Generation (RAG) focuses on individual documents during retrieval and is not suitable for networked documents.
Approach:	They propose a novel divide-and-conquer strategy that retrieves optimal subgraph structure in linear time.
Outcome:	The proposed approach outperforms current state-of-the-art methods on graph reasoning benchmarks.

CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling (2026.acl-long)

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Runsong Zhao, Shilei Liu, Jiwei Tang, Langming Liu, Haibin Chen, Weidong Zhang, Yujin Yuan, Tong Xiao, JingBo Zhu, Wenbo Su, Bo Zheng

Unveiling Opinion Evolution via Prompting and Diffusion for Short Video Fake News Detection (2024.findings-acl)

Challenge:	a novel architecture that enables LLMs to handle arbitrarily long sequences with constant memory usage and linear time complexity is a major barrier to long-context processing.
Approach:	They propose a novel architecture that enables LLMs to handle arbitrarily long sequences with constant memory usage and linear time complexity.
Outcome:	The proposed architecture can handle arbitrarily long sequences with constant memory usage and linear time complexity.

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Linlin Zong, Jiahui Zhou, Wenmin Lin, Xinyue Liu, Xianchao Zhang, Bo Xu

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

Challenge:	Existing methods for short video fake news detection ignore the implicit opinions and evolving nature of opinions across modalities.
Approach:	They propose a short video fake news model that mines implicit opinions within short videos and promotes the evolution of both explicit and implicit opinions across all modalities.
Outcome:	The proposed model outperforms existing methods on a publicly available dataset for short video fake news detection.

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

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

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

Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning (2026.acl-long)

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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Bo Li, Mingda Wang, Gexiang Fang, Shikun Zhang, Wei Ye

FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction (2023.acl-long)

Challenge:	Existing models that ground retrieval on external evidence are limited in their ability to implement retrieval-augmented generation.
Approach:	They propose a retrieval-augmented generation model that embeds retrieval control directly into generation.
Outcome:	The proposed model surpasses strong RAG baselines and uses substantially fewer parameters.

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Tianshuo Peng, Zuchao Li, Lefei Zhang, Bo Du, Hai Zhao

Supervised Treebank Conversion: Data and Approaches (P18-1)

Challenge:	Existing Universal Information Extraction models rely heavily on span boundaries in data during training, which does not reflect the reality of span annotation challenges.
Approach:	They propose a framework that uses fuzzy spans to model various IE tasks . they propose generative Universal Information Extraction (UIE) to unify various ie tasks based on fuzzy span boundaries .
Outcome:	The proposed framework improves on a series of main IE tasks with small amounts of data and training epochs.

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Xinzhou Jiang, Zhenghua Li, Bo Zhang, Min Zhang, Sheng Li, Luo Si

Retrieved In-Context Principles from Previous Mistakes (2024.emnlp-main)

Challenge:	Existing work on treebank conversion focuses on unsupervised treebanks . lack of manually labeled data means that sentences have two syntactic trees at the same time.
Approach:	They propose supervised treebank conversion using bi-tree aligned sentences . they propose two conversion approaches based on state-of-the-art deep biaffine parser .
Outcome:	The proposed method outperforms the state-of-the-art deep biaffine parser on the English WSJ dataset by 0.97 (93.76% -92.79%)

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Hao Sun, Yong Jiang, Bo Wang, Yingyan Hou, Yan Zhang, Pengjun Xie, Fei Huang

Low-Resource NLP

Challenge:	Recent advances in in-context learning (ICL) have limited customization and inadequate error coverage.
Approach:	They propose a method to retrieve in-context principles from mistakes to improve model performance.
Outcome:	The proposed framework enhances model performance when applied to various prompting strategies.

Zipage: Maintain High Request Concurrency for LLM Reasoning through Compressed PagedAttention (2026.findings-acl)

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Mengqi Liao, Lu Wang, Chaoyun Zhang, Bo Qiao, Si Qin, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Huaiyu Wan

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

Challenge:	Existing methods to evict KV cache during inference phase are impractical for industrial-grade applications.
Approach:	They propose a method that combines token-wise KV cache eviction with PagedAttention and propose 'zipage' it achieves 95% of the performance of Full KV inference engines while delivering over 2.1 speedup .
Outcome:	The proposed method achieves 95% of the performance of Full KV inference engines while delivering over 2.1 speedup on large-scale mathematical reasoning tasks.

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

Vector Calligrapher: Generating Scalable Vector Graphics via Structured Linguistic Supervision (2026.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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Bo Zhou, Xikang Chen, Yan Gong, Yin Zhang

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

Challenge:	Existing approaches to generate SVG-based fonts struggle with semantic ambiguity and inefficiency . edward mcginley: generic text tokenizers fragment coordinate-dense SVG XML into excessively long sequences .
Approach:	They propose a system that treats SVG generation as a conditional language modeling task . they propose linguistic supervision framework that decomposes typographic style into interpretable linguistic dimensions .
Outcome:	The proposed system improves CLIP score by +23% while reducing geometric error by 48% and boosts generation efficiency by 18% Command-per-Token (C/T) ratio.

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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

Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks (2020.acl-main)

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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Bo Zhang, Yue Zhang, Rui Wang, Zhenghua Li, Min Zhang

On Domain-Adaptive Post-Training for Multimodal Large Language Models (2025.findings-emnlp)

Challenge:	Opinion role labeling (ORL) is a fine-grained opinion analysis task . due to the scarcity of labeled data, ORL remains challenging for data-driven methods due to its complexity and complexity.
Approach:	They propose to integrate syntactic knowledge into ORL models by comparing and integrating different representations and using dependency graph convolutional networks to encode parser information at different processing levels.
Outcome:	The proposed model achieves 4.34 higher F1 score than the current state-of-the-art.

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Daixuan Cheng, Shaohan Huang, Ziyu Zhu, Xintong Zhang, Xin Zhao, Zhongzhi Luan, Bo Dai, Zhenliang Zhang

Improving Seq2Seq Grammatical Error Correction via Decoding Interventions (2023.findings-emnlp)

Challenge:	Adapting general multimodal large language models to specific domains is important for practical applications.
Approach:	They investigate domain adaptation of multimodal large language models via post-training . they develop a generate-then-filter pipeline that curates diverse visual instruction tasks .
Outcome:	The proposed model outperforms existing models in domain adaptation by combining data from open-source models with training pipelines.

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Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang

RoChBert: Towards Robust BERT Fine-tuning for Chinese (2022.findings-emnlp)

Challenge:	Existing approaches to grammatical error correction (GEC) are sequence-to-sequence and sequence-edit.
Approach:	They propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally.
Outcome:	The proposed framework outperforms baselines and state-of-the-art methods on English and Chinese datasets.

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Zihan Zhang, Jinfeng Li, Ning Shi, Bo Yuan, Xiangyu Liu, Rong Zhang, Hui Xue, Donghong Sun, Chao Zhang

AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images (2026.acl-long)

Challenge:	Pre-trained language models (e.g., BERT) have been proved vulnerable to adversarial texts.
Approach:	They propose to fuse Chinese phonetic and glyph features into pre-trained models by using a more comprehensive adversarial graph.
Outcome:	The proposed framework outperforms existing methods in significant ways on a wide range of tasks while remaining accurate on benign texts.

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Bo Zhang, Tzu-Yen Ma, Zichen Tang, Junpeng Ding, Zirui Wang, Yizhuo Zhao, Peilin Gao, Zijie Xi, Zixin Ding, Haiyang Sun, Haocheng Gao, Yuan Liu, Liangjia Wang, Yiling Huang, Yujie Wang, Yuyue Zhang, Ronghui Xi, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Haihong E

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

Challenge:	AEGIS examines whether current models can effectively audit AI-generated images in academic papers.
Approach:	They propose a holistic benchmark for forensic analysis of AI-Generated academic ImageS that reveals limitations in academic image forensics.
Outcome:	AEGIS compared with existing benchmarks on seven academic categories and features key advances in forensic analysis.

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

PretrainRL: Alleviating Factuality Hallucination of Large Language Models at the Beginning (2026.findings-acl)

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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Langming Liu, Kangtao Lv, Haibin Chen, Weidong Zhang, Yejing Wang, Shilei Liu, Xin Tong, Yujin Yuan, Yongwei Wang, Wenbo Su, Bo Zheng

Guangwei Zhang, Jianing Zhu, Cheng Qian, Neil Zhenqiang Gong, Rada Mihalcea, Zhaozhuo Xu, Jingrui He, Jiaqi W. Ma, Chaowei Xiao, Bo Li, Ahmed Abbasi, Dongwon Lee, Heng Ji, Denghui Zhang

Challenge:	Large language models suffer from factual hallucinations where they generate verifiable falsehoods.
Approach:	They propose a framework that integrates reinforcement learning into the pretraining phase to consolidate factual knowledge.
Outcome:	The proposed framework significantly alleviates factual hallucinations and outperforms state-of-the-art methods.

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Paraphrasing

Challenge:	Copyright Detective is the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs.
Approach:	They propose a system that detects copyright infringements and visualizes them . they use content recall testing, paraphrase-level similarity analysis and persuasive jailbreak probing .
Outcome:	The proposed system detects, analyzes, and visualizes potential copyright risks in LLM outputs.

Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data (P19-1)

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Wei Ye, Bo Li, Rui Xie, Zhonghao Sheng, Long Chen, Shikun Zhang

Towards Statistical Factuality Guarantee for Large Vision-Language Models (2025.emnlp-main)

Challenge:	Existing methods to perform relation extraction are feature-based or kernel-based, but the results of our study show that they can improve the performance of a baseline model with more than 10% absolute increase in F1-score.
Approach:	They propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss.
Outcome:	The proposed model outperforms the state-of-the-art models on ACE 2005 Chinese and English corpus and significantly improves the performance of a baseline model with more than 10% increase in F1-score.

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Zhuohang Li, Chao Yan, Nicholas J Jackson, Wendi Cui, Bo Li, Jiaxin Zhang, Bradley A. Malin

Entity Relation Extraction as Dependency Parsing in Visually Rich Documents (2021.emnlp-main)

Challenge:	Advancements in Large Vision-Language Models (LVLMs) have demonstrated impressive performance in image-conditioned text generation, but hallucinated outputs pose a major barrier to their use in safety-critical applications.
Approach:	They propose a conformal-prediction-based framework that achieves finite-sample distribution-free statistical guarantees to the factuality of LVLM output.
Outcome:	The proposed framework reduces the error rate of LLaVa-1.5 claims from 87.8% to 10.0% while ensuring that the output is accurate.

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Yue Zhang, Zhang Bo, Rui Wang, Junjie Cao, Chen Li, Zuyi Bao

Emotion Recognition in Conversation via Dynamic Personality (2024.lrec-main)

Challenge:	Existing studies on key information extraction from visually rich documents focus on labeling the text within bounding boxes, while relations between words are unexplored.
Approach:	They propose to use a dependency parsing model to extract semantic entities from visually rich documents by combining entity labeling and relation extraction tasks.
Outcome:	The proposed model achieves 65.96% F1 score on the FUNSD dataset.

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Yan Wang, Bo Wang, Yachao Zhao, Dongming Zhao, Xiaojia Jin, Jijun Zhang, Ruifang He, Yuexian Hou

SafeConf: A Confidence-Calibrated Safety Self-Evaluation Method for Large Language Models (2025.findings-emnlp)

Challenge:	Existing approaches to ERC focus on conversational contexts, but focus on static personality.
Approach:	They propose a model that considers the dynamic personality of speakers during conversations.
Outcome:	The proposed model outperforms existing models on three benchmark conversational datasets.

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Bo Zhang, Cong Gao, Linkang Yang, Bingxu Han, Minghao Hu, Zhunchen Luo, Guotong Geng, Xiaoying Bai, Jun Zhang, Wen Yao, Zhong Wang

RTCFake: Speech Deepfake Detection in Real-Time Communication (2026.findings-acl)

Challenge:	Large language models (LLMs) have many advantages but they also pose significant safety risks.
Approach:	They propose a method to enhance the safety self-evaluation capability of LLMs . they perform semantic mutations on the original safety evaluation questions .
Outcome:	The proposed method improves safety self-evaluation accuracy by 5.86% and 7.79% over baseline methods on Chinese and English datasets.

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Jun Xue, Zhuolin Yi, Yihuan Huang, Yanzhen Ren, Yujie Chen, Cunhang Fan, Zicheng Su, Yongcheng Zhang, Bo Cai

ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)

Challenge:	Existing detection studies focus on offline simulations and struggle to cope with complex distortions introduced during RTC transmission.
Approach:	They propose a large-scale speech deepfake dataset tailored for RTC scenarios . the dataset is constructed by transmitting speech through multiple social media and conferencing platforms .
Outcome:	The proposed dataset is constructed by transmitting speech through multiple mainstream social media and conferencing platforms, enabling precise pairing between offline and online speech.

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Jie Yang, Honglin Guo, Li Ji, Jiazheng Zhou, Rui Zheng, Zhikai Lei, Shuo Zhang, Zhiheng Xi, Shichun Liu, Yuxin Wang, Bo Wang, Yining Zheng, Tao Gui, Xipeng Qiu

A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement (2026.acl-long)

Challenge:	Large Language Models (LLMs) have redefined the role of AI in software engineering . current benchmarks focus on localized code generation, but neglect dynamic, full-process requirements of real-world engineering.
Approach:	They propose a benchmark to evaluate agentic backend coding within a realistic, executable workflow.
Outcome:	The ABC-Bench benchmark evaluates agentic backend coding within a realistic, executable workflow.

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Shengji Tang, Jianjian Cao, Weihao Lin, Jiale Hong, Bo Zhang, Shuyue Hu, Lei Bai, Tao Chen, Wanli Ouyang, Peng Ye

Take Its Essence, Discard Its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect (2024.lrec-main)

Challenge:	Existing multi-LLM collaboration systems often encounter scalability challenges when integrating new LLMs and tasks.
Approach:	They propose a Scalable Multi-LLM Collaboration System to coordinate multiple open-source LLMs.
Outcome:	The proposed system outperforms prevailing closed-source LLMs on eight mainstream benchmarks on multiple tasks.

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Junyu Lu, Bo Xu, Xiaokun Zhang, Kaiyuan Liu, Dongyu Zhang, Liang Yang, Hongfei Lin

mABC: Multi-Agent Blockchain-inspired Collaboration for Root Cause Analysis in Micro-Services Architecture (2024.findings-emnlp)

Challenge:	Existing methods to mitigate lexical bias in toxic language detection (TLD) do not exploit the “useful” and “misleading” impact of the bias.
Approach:	They propose a counterfactual Causal Debiasing Framework to mitigate lexical bias in toxic language detection (TLD) it preserves the “useful impact” of lexical bias and eliminates the "misleading impact" they propose to use the same framework to analyze the causal effect of a sentence and bias tokens .
Outcome:	The proposed framework preserves the “useful impact” of lexical bias and eliminates the ‘misleading impact’ Empirical evaluations show that the proposed model outperforms current debiased models for out-of-distribution data.

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Wei Zhang, Hongcheng Guo, Jian Yang, Zhoujin Tian, Yi Zhang, Yan Chaoran, Zhoujun Li, Tongliang Li, Xu Shi, Liangfan Zheng, Bo Zhang

Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation (2021.acl-long)

Challenge:	Root cause analysis (RCA) in Micro-services architectures with escalating complexity is challenging due to fault propagation and circular dependencies among nodes.
Approach:	They propose a framework where multiple agents follow Agent Workflow and collaborate in blockchain-inspired voting to ensure the reliability of root cause analysis.
Outcome:	The proposed framework reduces the number of steps and standardizes task processing through Agent Workflow.

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Tong Zhang, Long Zhang, Wei Ye, Bo Li, Jinan Sun, Xiaoyu Zhu, Wen Zhao, Shikun Zhang

PathwiseRAG: Multi-Dimensional Exploration and Integration Framework (2025.emnlp-main)

Challenge:	Existing approaches to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models have been criticized for lack of integration of bilingual lexical information into the neural architecture.
Approach:	They propose a neural architecture to incorporate bilingual dictionaries into Neural Machine Translation models by introducing three new components: Pointer, Disambiguator, and Copier.
Outcome:	The proposed method achieves the following merits inherently compared with previous efforts: (1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionary can potentially be used; (2) Disambiguator synthesizes contextual information from source view and target view, both of which contribute to distinguishing translation of a specific source word from multiple candidates in dicaries; (3) Copier systematically connects Pointer and Disambiguators based on a hierarchical

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Hengrui Zhang, Pin-Siang Huang, Zhen Zhang, Peican Lin, Yao-Ching Yu, Bo Hu, Yulu Du

Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers (2026.findings-acl)

Challenge:	Existing retrieval-augmented generation systems employ rigid retrieval strategies . static retrieval produces knowledge blind spots, missing connections between quantum algorithms and encryption vulnerabilities .
Approach:	PathwiseRAG addresses these challenges through intent-aware strategy selection . it constructs a directed acyclic graph of interconnected sub-problems and explores multiple reasoning trajectories .
Outcome:	The proposed framework achieves higher accuracy and better reliability than current systems.

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Zhiyang Zhang, Junda She, Kuo Cai, Bo Chen, Shiyao Wang, Xinchen Luo, Qiang Luo, Ruiming Tang, Han Li, Kun Gai, Guorui Zhou

SAE-SSV: Supervised Steering in Sparse Representation Spaces for Reliable Control of Language Models (2025.emnlp-main)

Challenge:	Existing methods for constructing item identifiers face bottlenecks due to their large output space and expensive vocabulary expansion and alignment training.
Approach:	They propose to use Large Language Models to develop general-purpose, semantically-aware recommender systems that can be generalized and reusable.
Outcome:	Experiments on real-world datasets show that GRAM outperforms baselines and significantly outperformed baselines.

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Zirui He, Mingyu Jin, Bo Shen, Ali Payani, Yongfeng Zhang, Mengnan Du

A Training-free LLM-based Approach to General Chinese Character Error Correction (2025.acl-long)

Challenge:	Large language models (LLMs) have impressive capabilities in natural language understanding and generation, but controlling their behavior remains a challenge.
Approach:	They propose a supervised steering approach that operates in sparse, interpretable representation spaces.
Outcome:	The proposed approach achieves higher success rates with minimal degradation in generation quality compared to existing methods.

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Houquan Zhou, Bo Zhang, Zhenghua Li, Ming Yan, Min Zhang

Coarse-to-Fine Grounded Memory for LLM Agent Planning (2025.emnlp-main)

Challenge:	Chinese spelling correction (CSC) is a crucial task that aims to correct character errors in text.
Approach:	They propose a task that handles missing and redundant characters and an additional prompt-based large language model to improve performance.
Outcome:	The proposed task is based on a high-quality dataset and a prompt-based large language model.

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Wei Yang, Jinwei Xiao, Hongming Zhang, Qingyang Zhang, Yanna Wang, Bo Xu

Challenge:	Existing methods to enhance LLM with offline experiences or online trajectory analysis focus on single-granularity memory derived from dynamic environmental interactions.
Approach:	They propose a framework that grounds coarse-to-fine memories with LLM to enable flexible adaptation to diverse scenarios.
Outcome:	Extensive experiments on AlfWorld, Webshop and ScienceWorld show that the proposed framework outperforms baselines and comprehensively optimizes memory-enhanced LLM Agent system.

Beyond A Fixed Seal: Adaptive Stealing Watermark in Large Language Models (2026.findings-acl)

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Shuhao Zhang, Yuli Chen, Jiale Han, Bo Cheng, Jiabao Ma

CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)

Challenge:	Existing detection methods for large language models rely on fixed strategies to steal watermarks.
Approach:	They propose a novel steal-based watermark algorithm that derives watermark information from watermarked texts to craft highly targeted adversarial attacks.
Outcome:	The proposed system significantly increases steal efficiency against target watermarks under identical conditions.

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Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, Dragomir Radev

Programming Languages in NLP

Challenge:	CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems.
Approach:	They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries .
Outcome:	The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization (2025.emnlp-main)

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Qi Zhang, Shouqing Yang, Lirong Gao, Hao Chen, Xiaomeng Hu, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Haobo Wang, Junbo Zhao

Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models (2025.coling-main)

Challenge:	Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL).
Approach:	They propose a process-level reward module to mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation.
Outcome:	The proposed framework can boost LLMs’ reasoning ability by integrating external knowledge sources through retrieval-augmented generation (RAG) The proposed model can mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation.

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Xinyu Pang, Ruixin Hong, Zhanke Zhou, Fangrui Lv, Xinwei Yang, Zhilong Liang, Bo Han, Changshui Zhang

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

Challenge:	Existing large language models (LLMs) fail due to lack of knowledge or incorrect knowledge application.
Approach:	They propose a knowledge-augmented framework that constructs a formula set to provide explicit physics knowledge and utilizes checklists to guide effective knowledge application.
Outcome:	The proposed framework achieves state-of-the-art performance on SciBench with an average accuracy improvement of 5.8%.

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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

P-QuASAR: A Unified Probabilistic Framework for Holistic Patent Quality Assessment and Refinement (2026.findings-acl)

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.

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Xinyuan Song, Ziyi Ni, Fred Yang, Bo Zhang, Yijin Wang, Jane Zhang

Structured Data in NLP

Challenge:	Existing methods for assessing patent quality rely on modular pipelines or generic detectors, resulting in fragmented decisions and limited integration across quality dimensions.
Approach:	They propose a probabilistic framework that represents patent specifications as Quality Graphs.
Outcome:	The proposed framework outperforms existing methods on 500 patents against seven baselines.

A Systematic Examination of Preference Learning through the Lens of Instruction-Following (2025.naacl-long)

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Joongwon Kim, Anirudh Goyal, Aston Zhang, Bo Xiong, Rui Hou, Melanie Kambadur, Dhruv Mahajan, Hannaneh Hajishirzi, Liang Tan

MTRouter: Cost-Aware Multi-Turn LLM Routing with History–Model Joint Embeddings (2026.acl-long)

Challenge:	a recent study has found that preference learning is a key tool for enhancing LLM training and alignment.
Approach:	They use a synthetic data generation pipeline to generate 48,000 unique instruction-following prompts with 23 verifiable constraints to obtain preference pairs.
Outcome:	The proposed pipeline generates 48,000 unique instruction-following prompts with 23 verifiable constraints that enable fine-grained and automated quality assessments of model responses.

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Yiqun Zhang, Hao Li, Zihan Wang, Shi Feng, Xiaocui Yang, Daling Wang, Bo Zhang, Lei Bai, Shuyue Hu

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

Challenge:	Multi-turn, long-horizon tasks require dozens of sequential model calls per episode.
Approach:	They propose a cost-aware multi-turn LLM routing tool which encodes interaction history and candidate models into joint history–model embeddings and learns an outcome estimator from logged trajectories to predict turn-level model utility.
Outcome:	The proposed model reduces cost and performance by 58.7% on ScienceWorld and on Humanity’s Last Exam (HLE) and even reduces costs for held-out tasks.

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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.

Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer (2020.coling-main)

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Duzhen Zhang, Xiuyi Chen, Shuang Xu, Bo Xu

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

Challenge:	Existing models for ERTC use a few non-neutral categories to identify the emotion of each utterance.
Approach:	They propose a novel Knowledge Aware Incremental Transformer with Multi-task Learning to address these challenges by leveraging commonsense knowledge to leverage context.
Outcome:	The proposed model outperforms state-of-the-art models across five benchmark datasets.

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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

GoT-R1: Internalizing Graph-of-Thought via Structural Reinforcement for High-Density Reasoning (2026.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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Zuchao Li, Qiwei Li, Yao Yao, Hai Zhao, Lefei Zhang, Bo Du

Dynamic Evil Score-Guided Decoding: An Efficient Decoding Framework For Red-Team Model (2025.findings-acl)

Challenge:	Chain-of-Thought reasoning suffers from an inherent mechanism flaw: linearity induces overthinking . emergence of Large Language Models (LLMs) has fundamentally redefined artificial intelligence .
Approach:	They propose a framework that replaces verbose linear trajectories with high-density reasoning graphs.
Outcome:	The proposed framework outperforms state-of-the-art models with reduced token overhead.

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Cong Gao, Bo Zhang, Linkang Yang, Minghao Hu, Zhunchen Luo, Xiaoying Bai, Guotong Geng, Jun Zhang, Yunhua Xue

CodeV: Issue Resolving with Visual Data (2025.findings-acl)

Challenge:	Existing red-teaming methods require expensive fine-tuning, especially for large LLMs.
Approach:	They propose a red-teaming method that uses an ‘evil score’ to evaluate the potential of tokens to contribute to harmful outputs during decoding.
Outcome:	The proposed method achieves an ASR of 92.83% on the Llama-3.2-3B-Instruct model, compared to 83.48% with adversarial fine-tuning while using less computational resources.

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Linhao Zhang, Daoguang Zan, Quanshun Yang, Zhirong Huang, Dong Chen, Bo Shen, Tianyu Liu, Yongshun Gong, Huang Pengjie, Xudong Lu, Guangtai Liang, Lizhen Cui, Qianxiang Wang

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

Challenge:	Large Language Models (LLMs) have expanded to more complex repository-level tasks.
Approach:	They propose a first approach to leveraging visual data to enhance the issue-resolving capabilities of Large Language Models (LLMs) they demonstrate the effectiveness of CodeV and provide valuable insights into leveraging visualization to resolve GitHub issues.
Outcome:	The proposed approach improves the issue-resolving capabilities of Large Language Models (LLMs) by using visual data.

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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

Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval (2025.acl-long)

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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Hao Sun, Yingyan Hou, Jiayan Guo, Bo Wang, Chunyu Yang, Jinsong Ni, Yan Zhang

On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval (2023.findings-emnlp)

Challenge:	Document retrieval in real-world scenarios faces significant challenges due to diverse document formats and modalities.
Approach:	They propose a visual-textual embedding framework that integrates textual and visual features for robust document representation.
Outcome:	The proposed visual-textual embedding framework surpasses existing methods while preserving semantic fidelity.

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Jiayi Chen, Hanjun Dai, Bo Dai, Aidong Zhang, Wei Wei

Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Models (2025.acl-long)

Challenge:	Visually-rich document entity retrieval (VDER) is an important topic in industrial NLP applications.
Approach:	They propose a task-aware meta-learning framework to tackle the problem of visually-rich document entity retrieval (VDER) they adopt a hierarchical decoder and employ contrastive learning to achieve this goal.
Outcome:	The proposed framework significantly improves the robustness of popular meta-learning baselines.

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Zihong Zhang, Liqi He, Zuchao Li, Lefei Zhang, Hai Zhao, Bo Du

Low-Resource NLP

Challenge:	Existing approaches to measure word segmentation only assess the language model's understanding of the overall meaning of sentences, lacking an evaluation of the language models' understanding capabilities at a fine-grained level.
Approach:	They propose a framework to explore the limit of unsupervised word segmentation with Large Language Models (LLMs) they employ current mainstream LLMs to perform word segmentations across multiple languages .
Outcome:	The proposed method improves on existing methods and combines the advanced pattern recognition capabilities of Aho-Corasick automata with the deep insights of well-pretrained LLMs.

Rank-Aware Negative Training for Semi-Supervised Text Classification (2023.tacl-1)

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Ahmed Murtadha, Shengfeng Pan, Wen Bo, Jianlin Su, Xinxin Cao, Wenze Zhang, Yunfeng Liu

Better Pre-Training by Reducing Representation Confusion (2023.findings-eacl)

Challenge:	Semi-supervised text classification-based paradigms employ the spirit of self-training, but the accuracy of pseudo-labels can be a problem in real-world scenarios.
Approach:	They propose a Rank-aware Negative Training framework to address SSTC in noisy label learning . they rank unlabeled texts based on evidential support from the labeled texts.
Outcome:	The proposed framework overcomes state-of-the-art alternatives and achieves competitive performance in other scenarios.

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Haojie Zhang, Mingfei Liang, Ruobing Xie, Zhenlong Sun, Bo Zhang, Leyu Lin

EvoWiki: Evaluating LLMs on Evolving Knowledge (2025.acl-long)

Challenge:	Existing methods to improve pre-trained language models address information confusion in position encoding and model representations.
Approach:	They propose two techniques to improve pre-trained language models by decoupling directions and auxiliary regularizers.
Outcome:	The proposed techniques can improve pre-trained language models on GLUE benchmarks.

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Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying, Bo Wang, Yizhe Yang, Yuyue Zhao, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Yong Liao

Challenge:	Existing knowledge evolution benchmarks are static and fail to capture the evolving nature of LLMs and knowledge.
Approach:	They propose an evolving dataset that categorizes information into stable, evolved, and uncharted states.
Outcome:	The proposed dataset is auto-updatable and enables evaluation of continuously changing knowledge and newly released LLMs.

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

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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

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

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

From spoken dialogue to formal summary: An utterance rewriting for dialogue summarization (2022.naacl-main)

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

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Yue Fang, Hainan Zhang, Hongshen Chen, Zhuoye Ding, Bo Long, Yanyan Lan, Yanquan Zhou

Text Generation

Challenge:	Existing models focus more on the structure of summary, not on the personal and logical inconsistency problem.
Approach:	They propose a model to solve the problem of personal and logical inconsistency . they use an utterance rewriter to complete the ellipsis content of dialogue content .
Outcome:	The proposed model outperforms baseline models on both SAMSum and DialSum datasets.

Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering (2023.emnlp-industry)

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Fangkai Yang, Pu Zhao, Zezhong Wang, Lu Wang, Bo Qiao, Jue Zhang, Mohit Garg, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

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

Challenge:	Large Language Models (LLMs) have gained popularity but lack specific domain knowledge in domain-specific tasks.
Approach:	They propose a model interaction paradigm that empowers LLM to achieve better performance on domain-specific tasks where it is not proficient.
Outcome:	The proposed approach outperforms the commonly used LLM with retrieval methods in domain-specific tasks.

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

BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026.acl-long)

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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Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang, Qingxuan Jiang, Fanghua Ye, Juntao Li, Zhaopeng Tu, Xiaolong Li, Liefeng Bo, Min Zhang

Speech & Audio in NLP

Challenge:	Existing approaches often fail to leverage the linguistic intelligence of Large Language Models (LLMs) Existing models lack the ability to follow text instructions for controllable Text-to-Speech (TTS).
Approach:	They propose a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan - explicit vocal features.
Outcome:	The proposed model outperforms open- and closed-source models in speech synthesis and achieves zero-shot cross-lingual generalization.

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

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

A Comparative Study of Explicit and Implicit Gender Biases in Large Language Models via Self-evaluation (2024.lrec-main)

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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Yachao Zhao, Bo Wang, Yan Wang, Dongming Zhao, Xiaojia Jin, Jijun Zhang, Ruifang He, Yuexian Hou

SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model (2025.acl-long)

Challenge:	Existing studies on the explicit and implicit biases in large language models (LLMs) focus on either explicit or implicit bias.
Approach:	They propose a self-evaluation-based two-stage measurement of explicit and implicit biases within large language models grounded in social psychology.
Outcome:	The proposed model is based on two stages of self-evaluation on state-of-the-art LLMs to measure explicit bias toward social targets, where bias is less likely to be self-recognized by the LLM.

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Xun Liang, Simin Niu, Zhiyu Li, Sensen Zhang, Hanyu Wang, Feiyu Xiong, Zhaoxin Fan, Bo Tang, Jihao Zhao, Jiawei Yang, Shichao Song, Mengwei Wang

Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning? (2025.acl-long)

Challenge:	Existing approaches to integrating external knowledge into large language models (LLMs) however, the incorporation of external knowledge increases the vulnerability of LLMs .
Approach:	They propose a benchmark to evaluate the RAG security using a dataset . they classify attack tasks into silver noise, inter-context conflict, soft ad, and white Denial-of-Service .
Outcome:	The proposed benchmark evaluates the security of RAG against 14 representative RAG components.

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Yancheng He, Shilong Li, Jiaheng Liu, Weixun Wang, Xingyuan Bu, Ge Zhang, Z.y. Peng, Zhaoxiang Zhang, Zhicheng Zheng, Wenbo Su, Bo Zheng

Speak No Evil, Just Prompt: Low-resource Multilingual Toxic Speech Detection with Audio Language Model (2026.findings-acl)

Challenge:	Recent advances in o1-like models have generated long Chain-of-Thought reasoning steps to improve the reasoning abilities of existing Large Language Models (LLMs).
Approach:	They propose a DeltaBench to analyze the quality and effectiveness of o1-like models and measure their ability to detect errors in long COT reasoning.
Outcome:	The proposed model can detect errors in long COT reasoning.

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Mingzi Zuo, Lei Zhang, Hailiang Sun, Shengzhi Huo, Changyu Dong, Xin Wang, Bo Wang, Hao Liu

CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion (2022.acl-long)

Challenge:	Existing methods for toxic speech detection rely on high-resource languages and lack acoustic cues.
Approach:	They propose a prompt-based adaptation framework that performs end-to-end toxicity detection without ASR.
Outcome:	The proposed framework achieves a micro-averaged ROC-AUC of 98.07% on polySpeechTox . it is based on a frozen audio language model and can perform end-to-end toxicity detection without ASR .

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Guanglin Niu, Bo Li, Yongfei Zhang, Shiliang Pu

Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks (2023.acl-long)

Challenge:	Existing knowledge graph embedding techniques rely on fact-view data to predict missing links between entities, limiting their performance.
Approach:	They propose a commonsense-aware knowledge embedding framework which generates commonsensense from factual triples with entity concepts for a KGC task.
Outcome:	The proposed framework could produce high-quality negative triples and joint commonsense and fact-view link prediction.

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Junyu Lu, Bo Xu, Xiaokun Zhang, Changrong Min, Liang Yang, Hongfei Lin

Challenge:	Existing datasets suffer from a lack of fine-grained annotations, such as the toxic type and expressions with indirect toxicity.
Approach:	They propose a benchmark model to detect toxic language by incorporating lexical features into a Chinese dataset to facilitate fine-grained annotations.
Outcome:	The proposed model is based on insulting vocabulary containing implicit profanity and is able to detect toxic language with lexical features.

Profanity-Avoiding Training Framework for Seq2seq Models with Certified Robustness (2021.emnlp-main)

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Hengtong Zhang, Tianhang Zheng, Yaliang Li, Jing Gao, Lu Su, Bo Li

Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training (2026.acl-long)

Challenge:	a recent study shows that inappropriate language can cause models to output profanity . authors propose a training framework to prevent such outputs from hurting the usability of models .
Approach:	proposed training framework eliminates the causes that trigger the generation of profanity . authors propose a framework that leverages a short list of profans to prevent this .
Outcome:	a proposed training framework can prevent models from generating profanity . the proposed framework leverages a short list of profanities examples .

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Jihao Gu, Qihang Ai, Yingyao Wang, Pi Bu, Jingxuan Xing, Yue Cao, Zekun Zhu, Wei Jiang, Ziming Wang, Yingxiu Zhao, Ming-Liang Zhang, Jun Song, Yuning Jiang, Bo Zheng

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

Challenge:	Existing approaches to training agents for visual-language models trap them in local optima, hindering exploration and error correction with the environment.
Approach:	They propose a hierarchical training recipe that bridges atomic action execution and strategic task completion.
Outcome:	The proposed training recipe bridges atomic action execution and strategic task completion.

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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

SpikeVoice: High-Quality Text-to-Speech Via Efficient Spiking Neural Network (2024.acl-long)

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.

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Kexin Wang, Jiahong Zhang, Yong Ren, Man Yao, Di Shang, Bo Xu, Guoqi Li

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

Challenge:	SpikeVoice performs high-quality Text-To-Speech (TTS) via SNN . major obstacle to using SNN for such generative tasks lies in the demand for models to grasp long-term dependencies.
Approach:	They propose a brain-inspired Spiking Neural Network (SNN) which performs high-quality Text-To-Speech (TTS) via SNN and explores the potential of SNN to "speak".
Outcome:	The proposed model achieves comparable results to Artificial Neural Networks (ANN) with only 10.5% energy consumption of ANN.

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