Papers by Yong Yu

Exploring Diverse Expressions for Paraphrase Generation (D19-1)

Challenge:	Existing methods for named entity recognition classify mentions into fixed set of predefined entity types but in many real-world scenarios, new entity types are incrementally involved.
Approach:	They propose a two-stage framework Learn-and-Review for continual named entity recognition to alleviate inter-type confusion.
Outcome:	The proposed framework outperforms the state-of-the-art method on CoNLL-03 and OntoNotes-5.0.

Copied to clipboard

Lihua Qian, Lin Qiu, Weinan Zhang, Xin Jiang, Yong Yu

Text Generation

Challenge:	Existing neural paraphrase generation methods focus on single paraphrases while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications.
Approach:	They propose a novel approach with two discriminators and multiple generators to generate a variety of different paraphrases.
Outcome:	The proposed model gains significant diversity and improves quality over state-of-the-art datasets.

Argumentation Mining on Essays at Multi Scales (2020.coling-main)

Copied to clipboard

Hao Wang, Zhen Huang, Yong Dou, Yu Hong

Syntactic Text Processing

Challenge:	Argumentation mining on essays is a new task in natural language processing.
Approach:	They propose a multi-scale argumentation mining model which aims to identify the types and locations of argumentation components from essay text.
Outcome:	The proposed model outperforms existing models on mining all types of argumentation components on the Persuasive Essay dataset.

DebateCoder: Towards Collective Intelligence of LLMs via Test Case Driven LLM Debate for Code Generation (2025.acl-long)

Copied to clipboard

Jizheng Chen, Kounianhua Du, Xinyi Dai, Weiming Zhang, Xihuai Wang, Yasheng Wang, Ruiming Tang, Weinan Zhang, Yong Yu

Programming Languages in NLP

Challenge:	Existing debate-based approaches to code generation are limited due to several reasons: 1) Reliance on different instances of the same LLM for debate, 2) under-utilization of test cases, and 3) reliance on third-party moderators for result consolidation and decision-making.
Approach:	They propose to use test cases to analyze code and identify bugs while opposing models generate test cases for each other to challenge each other's code during the debate process.
Outcome:	The proposed model collects intelligence of LLMs via test case-driven debate for code generation.

PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation (2022.coling-1)

Copied to clipboard

Juncheng Wan, Jian Yang, Shuming Ma, Dongdong Zhang, Weinan Zhang, Yong Yu, Zhoujun Li

Text Generation

Challenge:	Existing methods for adversarial example generation are word-level or character-level, which ignore the ubiquitous phrase structure.
Approach:	They propose a phrase-level adversarial example generation framework to enhance the robustness of the translation model by adopting a sentence-level substitution strategy.
Outcome:	The proposed method improves translation performance and robustness to noise on three benchmarks.

EXPLAIN: Enhancing Retrieval-Augmented Generation with Entity Summary (2025.acl-industry)

Copied to clipboard

Yaozhen Liang, Xiao Liu, Jiajun Yu, Zhouhua Fang, Qunsheng Zou, Linghan Zheng, Yong Li, Zhiwei Liu, Haishuai Wang

Explainability & Interpretability in NLP

Challenge:	Existing document question answering methods reduce inference costs and input tokens.
Approach:	They propose a retrieval-augmented generation method that automatically extracts useful entities and generates summaries from documents.
Outcome:	The proposed method surpasses baseline retrieval-augmented generation (RAG) and long-context question answering (LC) methods achieve higher accuracy by processing entire documents, but at the cost of increased computational Corresponding authors.

Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space (2020.findings-emnlp)

Copied to clipboard

Dongyu Ru, Jiangtao Feng, Lin Qiu, Hao Zhou, Mingxuan Wang, Weinan Zhang, Yong Yu, Lei Li

LaTeX2Solver: a Hierarchical Semantic Parsing of LaTeX Document into Code for an Assistive Optimization Modeling Application (2023.acl-demo)

Challenge:	Existing uncertainty sampling methods are time-consuming and can't be executed frequently.
Approach:	They propose adversarial uncertainty sampling in discrete space to find informative unlabeled text samples for annotation using adversarials.
Outcome:	The proposed approach outperforms baselines on effectiveness on five datasets.

Copied to clipboard

Rindra Ramamonjison, Timothy Yu, Linzi Xing, Mahdi Mostajabdaveh, Xiaorui Li, Xiaojin Fu, Xiongwei Han, Yuanzhe Chen, Ren Li, Kun Mao, Yong Zhang

Semantic Text Processing

Challenge:	Existing systems that translate optimization formulas manually are cumbersome and time-consuming.
Approach:	They propose a system that converts optimization formulas from TeX document to solver language.
Outcome:	The proposed system helps operations research practitioners convert optimization formulations into solver modeling languages.

Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition (N18-1)

Copied to clipboard

Zhenghui Wang, Yanru Qu, Liheng Chen, Jian Shen, Weinan Zhang, Shaodian Zhang, Yimei Gao, Gen Gu, Ken Chen, Yong Yu

Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks (2023.findings-acl)

Challenge:	NER is a fundamental problem for medical text mining because of the difference of specialties and cost of human annotation.
Approach:	They propose a label-aware double transfer learning framework for medical NER from electronic medical records.
Outcome:	The proposed framework improves accuracy over strong baselines on 12 cross-specialty NER tasks.

Copied to clipboard

Xuming Hu, Yong Jiang, Aiwei Liu, Zhongqiang Huang, Pengjun Xie, Fei Huang, Lijie Wen, Philip S. Yu

Information Extraction & Text Mining

Challenge:	Existing augmentation techniques manipulate words in the original text that break the semantic coherence of the text, or exploit generative models that ignore preserving entities in the text.
Approach:	They propose a novel Entity-to-Text based data augmentation technique called EnTDA to add, delete, replace or swap entities in the original text.
Outcome:	The proposed technique generates semantically coherent and entity preserving texts on thirteen NER tasks and two settings.

Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models (2024.findings-acl)

Copied to clipboard

Chen Qian, Jie Zhang, Wei Yao, Dongrui Liu, Zhenfei Yin, Yu Qiao, Yong Liu, Jing Shao

Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation (2025.findings-acl)

Challenge:	Existing studies focus on pre-trained LLMs to better understand and improve their trustworthiness.
Approach:	They apply linear probing to LLMs to explore five key dimensions of trustworthiness: reliability, privacy, toxicity, fairness, and robustness.
Outcome:	The proposed model can distinguish concepts in each trustworthiness dimension, suggesting that it can be trained in early pre-training.

Copied to clipboard

Kounianhua Du, Hanjing Wang, Jianxing Liu, Jizheng Chen, Xinyi Dai, Yasheng Wang, Ruiming Tang, Yong Yu, Jun Wang, Weinan Zhang

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

Challenge:	Existing systems 2 methods for code generation are difficult to implement due to the complex hidden reasoning process and heterogeneous data distribution.
Approach:	They propose a framework that Boosts reasoning exploration via multi-agent collaboration and Disentangles heterogeneous data into specialized experts.
Outcome:	The proposed framework outperforms state-of-the-art methods on APPS and CodeContest benchmarks and achieves 73.8% accuracy on hard problems.

Copied to clipboard

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.

FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models (2023.findings-acl)

Copied to clipboard

Zhuo Zhang, Yuanhang Yang, Yong Dai, Qifan Wang, Yue Yu, Lizhen Qu, Zenglin Xu

WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models (2024.acl-long)

Challenge:	Existing research on federated learning (FL) for pre-trained language models (PLMs) with increasing concerns about data privacy, enterprises or institutions are not allowed to collect data from end devices or local clients to a centralized server for fine-tuning PLMs.
Approach:	They investigate the parameter-efficient tuning of pre-trained language models (PLMs) and develop a federated benchmark for four representative PETuning methods .
Outcome:	The proposed method can defend against privacy attacks and maintain acceptable performance with reducing heavy resource consumption.

Copied to clipboard

Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Yong Dai, Hongming Zhang, Zhenzhong Lan, Dong Yu

Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions (2022.emnlp-industry)

Challenge:	Existing web agents only handle one input modality and are evaluated only in simplified web simulators or static web snapshots, greatly limiting their applicability in real-world scenarios.
Approach:	They propose a large multimodal model-powered web agent that can complete user instructions end-to-end by interacting with real-world websites.
Outcome:	The proposed agent achieves 59.1% task success rate, surpassing both GPT-4 and WebVoyager setups.

Copied to clipboard

Rindra Ramamonjison, Haley Li, Timothy Yu, Shiqi He, Vishnu Rengan, Amin Banitalebi-dehkordi, Zirui Zhou, Yong Zhang

Challenge:	Existing systems for operations research use NLP to suggest formulations of optimization problems.
Approach:	They propose an augmented intelligence system that can be used to simplify and enhance the modeling experience for operations research.
Outcome:	The proposed system validates and edits the proposed formulations with a dataset of linear programming problems drawn from various application domains.

Nested Named Entity Recognition with Span-level Graphs (2022.acl-long)

Copied to clipboard

Juncheng Wan, Dongyu Ru, Weinan Zhang, Yong Yu

Information Extraction & Text Mining

Challenge:	Named entity recognition is one of the major subtasks of information extraction for extracting categorized named entities from unstructured text.
Approach:	They propose to use retrieval-based span-level graphs to connect spans and entities in the training data based on n-gram features to integrate information of similar neighbor entities into the span representation.
Outcome:	The proposed method achieves general improvements on all three benchmarks and special superiority on low frequency entities.

RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation (2025.emnlp-main)

Copied to clipboard

Qingyao Li, Wei Xia, Xinyi Dai, Kounianhua Du, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Yu, Weinan Zhang

Programming Languages in NLP

Challenge:	Existing tree search methods neglect the underlying reasoning process, resulting in poor search quality.
Approach:	They propose a framework that systematically explores and refines the reasoning process for code generation by using a tree search engine and a reflection mechanism.
Outcome:	The proposed framework outperforms existing methods in the code generation domain.

Progressive LoRA for Multimodal Continual Instruction Tuning (2025.findings-acl)

Copied to clipboard

Yahan Yu, Duzhen Zhang, Yong Ren, Xuanle Zhao, Xiuyi Chen, Chenhui Chu

CodePRM: Execution Feedback-enhanced Process Reward Model for Code Generation (2025.findings-acl)

Challenge:	Existing approaches to MCIT address Catastrophic Forgetting and Knowledge Transfer (KT) but using a fixed number of shared LoRA blocks across tasks can lead to knowledge interference.
Approach:	They propose a framework that uses a fixed number of shared LoRA blocks to reduce knowledge interference.
Outcome:	The proposed framework outperforms existing approaches on the latest MCIT benchmark.

Copied to clipboard

Qingyao Li, Xinyi Dai, Xiangyang Li, Weinan Zhang, Yasheng Wang, Ruiming Tang, Yong Yu

Programming Languages in NLP

Challenge:	Recent advances in code generation focus on optimizing the thought process, but lack effective process supervision, making it difficult to optimize the thoughts.
Approach:	They propose a method that leverages the code execution feedback to build a code PRM by collecting a large dataset of thought traces and then training it to take both the reasoning process and code execution as input.
Outcome:	The proposed approach outperforms baselines and strong LLMs in the inference stage.

Learning Logic Rules for Document-Level Relation Extraction (2021.emnlp-main)

Copied to clipboard

Dongyu Ru, Changzhi Sun, Jiangtao Feng, Lin Qiu, Hao Zhou, Weinan Zhang, Yong Yu, Lei Li

Information Extraction & Text Mining

Challenge:	Existing models for document-level relation extraction relied on implicitly powerful representations, which makes the model less transparent.
Approach:	They propose a probabilistic model for document-level relation extraction by learning logic rules.
Outcome:	The proposed model outperforms baseline models in relation performance and logical consistency.

Towards Emotional Support Dialog Systems (2021.acl-long)

Copied to clipboard

Siyang Liu, Chujie Zheng, Orianna Demasi, Sahand Sabour, Yu Li, Zhou Yu, Yong Jiang, Minlie Huang

Dialogue Systems & Conversational Agents

Challenge:	Emotional support is a crucial ability for many conversation scenarios, including social interactions, mental health support, and customer service chats.
Approach:	They propose an Emotional Support Conversation task and an ESC Framework to train emotional support into dialog systems.
Outcome:	The proposed framework provides an example of an Emotional Support Conversation task and shows that it is more effective than existing models.

Dynamically Fused Graph Network for Multi-hop Reasoning (P19-1)

Copied to clipboard

Lin Qiu, Yunxuan Xiao, Yanru Qu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu

WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoning (2026.findings-acl)

Challenge:	Text-based question answering (TBQA) has been studied extensively in recent years.
Approach:	They propose a Dynamically Fused Graph Network to answer questions requiring multiple scattered evidence and reasoning over them.
Outcome:	The proposed method achieves competitive results on a public TBQA dataset and produces interpretable reasoning chains.

Copied to clipboard

Xinmiao Yu, Liwen Zhang, Xiaocheng Feng, Pengjun Xie, Jingren Zhou, Bing Qin, Yong Jiang

Glancing Transformer for Non-Autoregressive Neural Machine Translation (2021.acl-long)

Challenge:	Existing methods for reinforcement learning (RL)-based agents struggle with long-horizon planning and strategy coherence.
Approach:	They propose a reinforcement learning framework that decouples planning and execution.
Outcome:	The proposed framework outperforms baseline and first-step RL frameworks on four benchmarks.

Copied to clipboard

Lihua Qian, Hao Zhou, Yu Bao, Mingxuan Wang, Lin Qiu, Weinan Zhang, Yong Yu, Lei Li

CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory (2025.acl-long)

Challenge:	Existing non-autoregressive neural machine translation methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup.
Approach:	They propose a Glancing Language Model (GLM) for single-pass parallel generation models and Glancing Transformer (GLAT) with only single- pass decoding, GLAT is able to generate high-quality translation with 8-15 speedup.
Outcome:	The proposed model outperforms all previous non-autoregressive methods on multiple language directions and is nearly comparable to Transformer.

Copied to clipboard

Weichen Zhang, Chen Gao, Shiquan Yu, Ruiying Peng, Baining Zhao, Qian Zhang, Jinqiang Cui, Xinlei Chen, Yong Li

Enhancing Multimodal Continual Instruction Tuning with BranchLoRA (2025.acl-long)

Challenge:	Existing ground VLN agents struggle in aerial VLLN due to the lack of predefined navigation graphs and the exponentially expanding action space in long-horizon exploration.
Approach:	They propose a large language model-empowered aerial VLN agent that decomposes the long-horizon task into sub-goals with different semantic levels.
Outcome:	The proposed method achieves state-of-the-art performance with significant improvement in continuous city environments.

Copied to clipboard

Duzhen Zhang, Yong Ren, Zhong-Zhi Li, Yahan Yu, Jiahua Dong, Chenxing Li, Zhilong Ji, Jinfeng Bai

Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning (2025.findings-acl)

Challenge:	Existing approaches to fine tune Multimodal Large Language Models (MLLMs) are prone to Catastrophic Forgetting (CF) existing approaches rely on the Mixture-of-Experts (MoE) LoRA framework to preserve previous instruction alignments.
Approach:	They propose an asymmetric tuning-freezing mechanism to mitigate parameter inefficiency . branch-specific routers are introduced to ensure optimal branch distribution over time .
Outcome:	The proposed framework outperforms existing frameworks on the latest MCIT benchmarks.

Copied to clipboard

Jiachen Zhu, Congmin Zheng, Jianghao Lin, Kounianhua Du, Ying Wen, Yong Yu, Jun Wang, Weinan Zhang

Reasoning

Challenge:	Large language models (LLMs) have advanced mathematical reasoning, but they still struggle with out-of-distribution (OOD) issues.
Approach:	They propose a framework to evaluate the logical validity of reasoning steps . they retrieves semantically similar questions and steps for PRM as a warmup .
Outcome:	The proposed framework outperforms baseline models on multiple real-world datasets.

Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures inside Arguments (2022.coling-1)

Copied to clipboard

Yu Zhang, Qingrong Xia, Shilin Zhou, Yong Jiang, Guohong Fu, Min Zhang

Syntactic Text Processing

Challenge:	Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based.
Approach:	They propose to regard flat argument spans as latent subtrees, thus reducing SRL to a tree parsing task.
Outcome:	The proposed model performs better than previous syntax-agnostic models on CoNLL05 and CoNll12 benchmarks.

CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks (2026.acl-long)

Copied to clipboard

Lingyue Fu, Hao Guan, Bolun Zhang, Haowei Yuan, Yaoming Zhu, Lin Qiu, ZongYu Wang, Xuezhi Cao, Xunliang Cai, Weiwen Liu, Weinan Zhang, Yong Yu

Challenge:	Existing large language models for software engineering rely on coarse-grained pass rates obscuring specific cognitive bottlenecks.
Approach:	They propose a repository-level benchmark that dissects coding capabilities through atomized tasks.
Outcome:	The proposed framework achieves a 78.55% validity yield, surpassing the 31.7% retention rate of SWE-bench-Verified.

A Survey of Large Language Model-Based Search Agents (2026.acl-long)

Copied to clipboard

Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, Weinan Zhang

Semantic Text Processing

Challenge:	Large Language Models (LLMs) have revolutionized web search, but their integration is static and cannot handle complex contexts.
Approach:	They analyze existing research and analyze existing work from the perspectives of architecture, optimization, application, and evaluation.
Outcome:	The proposed models can comprehend user intentions and context and execute multi-turn retrieval with dynamic planning, extending search capabilities far beyond the web.

A Comprehensive Survey of Process Reward Models: Data Generation, Model Construction, and Usage (2026.acl-long)

Copied to clipboard

Congmin Zheng, Jiachen Zhu, Zhuoying Ou, Yuxiang Chen, Kangning Zhang, Rong Shan, Zeyu Zheng, Mengyue Yang, Jianghao Lin, Yong Yu, Weinan Zhang

Reasoning

Challenge:	Large Language Models (LLMs) have advanced reasoning ability, yet conventional alignment remains dominated by outcome reward models that judge only final answers.
Approach:	They summarize applications across math, code, text, multimodal reasoning, robotics, and agents . goal is to clarify design spaces, reveal open challenges, and guide future research toward fine-grained, robust reasoning alignment.
Outcome:	The proposed model enables finer credit assignment, richer diagnostics, and improved robustness.

LoopTool: Closing the Data–Training Loop for Robust LLM Tool Calls (2026.acl-long)

Copied to clipboard

Kangning Zhang, Weiwen Liu, Wenxiang Jiao, Kounianhua Du, Yuan Lu, Weinan Zhang, Yong Yu