Papers by Yong Lin
Exploring Diverse Expressions for Paraphrase Generation (D19-1)
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| 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. |
MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation (2024.acl-long)
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Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie Zhou, Juanzi Li
| Challenge: | Existing datasets for event understanding have limited coverage due to complexity of tasks. |
| Approach: | They propose a dataset that augments MAVEN datasets with event argument annotations . they propose 98,591 events and 290,613 arguments obtained with laborious human annotation . |
| Outcome: | The proposed dataset is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction. |
The Instinctive Bias: Spurious Images lead to Illusion in MLLMs (2024.emnlp-main)
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| Challenge: | Existing multi-modal large language models (MLLMs) are able to process visual inputs by converting them into visual tokens that share the same latent space as language tokens in LLMs. |
| Approach: | They propose a benchmark that assesses the visual illusion level given spurious images and a pipeline that converts visual inputs into visual tokens. |
| Outcome: | The proposed benchmark shows that MLLMs suffer from an instinctive bias to varying degrees when presented with spurious images. |
Mitigating the Alignment Tax of RLHF (2024.emnlp-main)
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Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang
| Challenge: | Large Language Models (LLMs) acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, which is also known as the alignment tax. |
| Approach: | They propose to use a model averaging technique to find the most powerful alignment-forging Pareto front among RLHF algorithms. |
| Outcome: | The proposed method achieves the strongest alignment-forging Pareto front among competing methods. |
ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5 (2025.acl-long)
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Jiaming Zhou, Shiyao Wang, Shiwan Zhao, Jiabei He, Haoqin Sun, Hui Wang, Cheng Liu, Aobo Kong, Yujie Guo, Xi Yang, Yequan Wang, Yonghua Lin, Yong Qin
| Challenge: | Automatic speech recognition systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0. |
| Approach: | They propose to use Mandarin speech datasets to analyze pronunciation and tone of children aged 3 to 5 and evaluate their models on speaker verification (SV) They find that the datasets are more robust than those used by adult speech recognition systems and are open-source and available for all academic purposes. |
| Outcome: | The proposed dataset includes 41.25 hours of speech with carefully crafted manual transcriptions, collected from 397 speakers across various provinces in China, with balanced gender representation. |
Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments (2024.findings-acl)
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Sitao Cheng, Ziyuan Zhuang, Yong Xu, Fangkai Yang, Chaoyun Zhang, Xiaoting Qin, Xiang Huang, Ling Chen, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
| Challenge: | Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graphs and tables. |
| Approach: | They propose a framework that allows LLMs to efficiently and faithfully reason over structured environments. |
| Outcome: | The proposed framework surpasses state-of-the-art fine-tuned methods on three KGQA and two TableQA datasets and surpasse CWQ and WTQ methods. |
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space (2020.findings-emnlp)
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| 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. |
VIGIL: Defending LLM Agents Against Tool-Stream Injection via Verify-Before-Commit (2026.acl-long)
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| Challenge: | Existing defenses for indirect prompt injection are limited by static protection mechanisms . existing models prioritize injected rules due to strict alignment, whereas static protections sever the feedback loop required for adaptive reasoning. |
| Approach: | They propose a framework that shifts the paradigm from restrictive isolation to a verify-before-commit protocol. |
| Outcome: | The proposed framework outperforms state-of-the-art dynamic defenses by reducing the attack success rate by over 22% while more thandoubling utility under attack compared to static baselines. |
Visually-Guided Policy Optimization for Multimodal Reasoning (2026.acl-long)
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| Challenge: | Existing RLVRs lack visual faithfulness due to text-dominated reasoning . a novel framework to reinforce visual focus during policy optimization is proposed . |
| Approach: | They propose a framework to reinforce visual focus during policy optimization using visual attention compensation mechanism. |
| Outcome: | The proposed framework exhibits better visual activation and superior performance in multimodal reasoning and visual-dependent tasks. |
Active Prompting with Chain-of-Thought for Large Language Models (2024.acl-long)
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| Challenge: | Existing methods to annotate large language models rely on a fixed set of human-annotated exemplars, which are not always the most effective for different tasks. |
| Approach: | They propose a method to adapt large language models to different tasks with task-specific example prompts (annotated with human-designed CoT reasoning) they introduce several metrics to characterize uncertainty so as to select the most uncertain questions for annotation. |
| Outcome: | The proposed method significantly improves performance on eight complex reasoning tasks. |
Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)
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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. |
Unsupervised Domain Adaptation Method with Semantic-Structural Alignment for Dependency Parsing (2021.findings-emnlp)
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| Challenge: | Existing methods for dependency parsing are often of the pseudo-annotation type, but they fail to consider the change of model structure for domain adaptation. |
| Approach: | They propose a method that accomplishes unsupervised cross-domain dependency parsing without using labeled data. |
| Outcome: | The proposed method achieves consistent performance improvement on CODT1 and CTB9 domains. |
Beyond Templates: Dynamic Adaptation of Reasoning Demonstrations via Feasibility-Aware Exploration (2026.findings-acl)
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| Challenge: | Existing reasoning datasets that are designed for powerful LLMs often lead to degraded performance when directly applied to weaker models. |
| Approach: | They propose a data adaptation framework that bridges the capability gap between expert reasoning trajectories and diverse SLMs by employing a selective imitation strategy guided by step-wise adaptability estimation via solution simulation. |
| Outcome: | The proposed framework improves generalization and data efficiency over static fine-tuning and can be applied to large models with limited model capacity. |
Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards (2024.acl-long)
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| Challenge: | Reinforcement Learning from Human Feedback (RLHF) relies on scalar rewards to capture user preferences. |
| Approach: | They propose a framework that integrates multi-objective reward modeling to represent diverse preference profiles. |
| Outcome: | The proposed method improves performance across reward objectives and targets. |
ICL-Bandit: Relevance Labeling in Advertisement Recommendation Systems via LLM (2025.findings-emnlp)
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Lu Wang, Chiming Duan, Pu Zhao, Fangkai Yang, Yong Shi, Xuefeng Luo, Bingjing Xu, Weiwei Deng, Qingwei Lin, Dongmei Zhang
| Challenge: | In-context learning (ICL) is a common practice to enhance LLM performance on domain-specific tasks. |
| Approach: | They propose a method that leverages large language models to enhance query-ad relevance labeling . they identify and provide superior demonstrations for ICL, thereby improving labeling performance . |
| Outcome: | The proposed method improves query-ad relevance labeling performance by providing demonstrations. |
R-Tuning: Instructing Large Language Models to Say ‘I Don’t Know’ (2024.naacl-long)
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Hanning Zhang, Shizhe Diao, Yong Lin, Yi Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, Tong Zhang
| Challenge: | Existing methods for instruction tuning force the model to complete a sentence no matter whether it knows the knowledge or not. |
| Approach: | They propose a new approach to tuning large language models to refrain from answering questions beyond its parametric knowledge by identifying the disparity in parametric and parametric information. |
| Outcome: | The proposed approach improves a model’s ability to answer known questions and refrain from answering unknown questions. |
Learning Logic Rules for Document-Level Relation Extraction (2021.emnlp-main)
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| 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. |
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation (2024.findings-acl)
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Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang
| Challenge: | Recent advances in Large Language Models (LLMs) have greatly advanced problem solving in diverse domains such as mathematical reasoning and knowledge reasoning. |
| Approach: | They propose a thought prompting approach called 'Everything of Thoughts' it leverages pretrained reinforcement learning and Monte Carlo Tree Search to incorporate external domain knowledge and planning capability into thoughts. |
| Outcome: | The proposed approach outperforms existing approaches on game of 24, 8-Puzzle, and Pocket Cube. |
Dynamically Fused Graph Network for Multi-hop Reasoning (P19-1)
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| 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. |
Relation-aware Ensemble Learning for Knowledge Graph Embedding (2023.emnlp-main)
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| Challenge: | Existing methods to explore semantics of knowledge graphs have been proposed to explore these semantics in distinct ways. |
| Approach: | They propose to leverage existing methods in relation-aware manner to learn an ensemble by leveraging existing methods. |
| Outcome: | The proposed method has the same computation cost as general ensemble methods but with much better performance on benchmark datasets. |
Glancing Transformer for Non-Autoregressive Neural Machine Translation (2021.acl-long)
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| 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. |
Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models (2026.acl-long)
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| Challenge: | Semi-autoregressive (Semi-AR) decoding suffers from inherent block constraints . naive lookahead decoding is unreliable, token stability closely correlates with convergence trend, and historical information is isolated. |
| Approach: | They propose a training-free, plug-and-play dynamic decoding strategy that monitors the stability of tokens in real time through dynamic anchors. |
| Outcome: | The proposed approach reduces decoding steps by 80% while improving performance by 3.67% on the BBH benchmark. |
Revisiting Weak-to-Strong Generalization in Theory and Practice: Reverse KL vs. Forward KL (2025.findings-acl)
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| Challenge: | Weak-to-strong generalization is a promising approach to guide stronger systems, but its effectiveness is constrained by the inherent imperfections of weak model supervision. |
| Approach: | They propose a theoretically grounded approach that replaces forward KL divergence with reverse KL, which prioritizes high-confidence predictions. |
| Outcome: | The proposed approach replaces forward KL divergence with reverse KL, reducing the influence of unreliable weak supervision. |
Dependency Parsing via Sequence Generation (2022.findings-emnlp)
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| Challenge: | Existing methods for dependency parsing are transition-based, graph-based and sequence-to-sequence method. |
| Approach: | They propose to achieve dependency parsing (DP) via Sequence Generation (SG) by utilizing only the pre-trained language model without any auxiliary structures. |
| Outcome: | The proposed method performs well on DP benchmarks including PTB, UD2.2, SDP15 and SemEval16. |
Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning (2025.findings-acl)
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| 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. |
CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks (2026.acl-long)
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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. |
On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization (2024.findings-emnlp)
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Yong Lin, Skyler Seto, Maartje Ter Hoeve, Katherine Metcalf, Barry-John Theobald, Xuan Wang, Yizhe Zhang, Chen Huang, Tong Zhang
| Challenge: | Reinforcement Learning from Human Feedback (RLHF) is an effective approach for aligning language models to human preferences. |
| Approach: | They compare the accuracy of DPORM and EXRM with a reward function for scoring human preferences. |
| Outcome: | The proposed methods can approximate an EXRM on the limit infinite samples, but it is unclear how effective they are in practice. |
A Survey of Large Language Model-Based Search Agents (2026.acl-long)
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Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, Weinan Zhang
| 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)
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Congmin Zheng, Jiachen Zhu, Zhuoying Ou, Yuxiang Chen, Kangning Zhang, Rong Shan, Zeyu Zheng, Mengyue Yang, Jianghao Lin, Yong Yu, Weinan Zhang
| 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. |
DiningBench: A Hierarchical Multi-view Benchmark for Perception and Reasoning in the Dietary Domain (2026.acl-long)
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| Challenge: | Existing vision-language models lack fine-grained classification, single-view imagery, and inaccurate metadata. |
| Approach: | They propose a hierarchical, multi-view benchmark to evaluate VLMs across three levels of cognitive complexity. |
| Outcome: | The proposed benchmark evaluates vision-language models across three levels of complexity . it systematically identifies five primary failure modes . the proposed benchmarks are available on https://github.com/meituan/DiningBench. |
Hate Speech Detection Based on Sentiment Knowledge Sharing (2021.acl-long)
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| Challenge: | Existing methods for hate speech detection are stereotyped and biased . et al., a paper examining the effectiveness of multitask learning in hate speech recognition tasks . |
| Approach: | They propose a hate speech detection framework based on sentiment knowledge sharing . they extract affective features of the target sentence and use sentiment features from external resources . |
| Outcome: | The proposed model can detect hate speech over two public datasets. |