Papers by Yijun Wang
Multiview Clickbait Detection via Jointly Modeling Subjective and Objective Preference (2023.findings-emnlp)
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| Challenge: | Existing clickbait detection models rely on analyzing the objective semantics of posts or correlating posts with article content only, but fail to identify and exploit the manipulation intention of clickbaiting from a user’s subjective perspective. |
| Approach: | They propose a multiview clickbait detection model to model subjective and objective preferences simultaneously to capture clickbaiting from a user's subjective perspective. |
| Outcome: | The proposed model outperforms state-of-the-art models on two real-world datasets and shows that it integrates subjective and objective preferences simultaneously. |
Lookahead Q-Cache: Achieving More Consistent KV Cache Eviction via Pseudo Query (2025.emnlp-main)
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| Challenge: | Existing KV cache eviction methods prune tokens using prefilling-stage attention scores, causing inconsistency with actual inference queries. |
| Approach: | They propose a lookahead q-cache framework that generates low-cost pseudo lookaheaded queries to better approximate the true decoding-stage queries. |
| Outcome: | The proposed framework outperforms existing methods on LongBench and Needle-in-a-Haystack benchmarks and can be flexibly combined to yield further improvements. |
Pre-training Entity Relation Encoder with Intra-span and Inter-span Information (2020.emnlp-main)
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| Challenge: | Existing pre-trained models do not handle text spans and relation among text span pairs. |
| Approach: | They propose to integrate span-related information into pre-trained encoder for entity relation extraction task. |
| Outcome: | The proposed pre-training method outperforms distantly supervised pre-trained models on two entity relation extraction benchmark datasets. |
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training (2024.emnlp-main)
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Yixuan Wang, Xianzhen Luo, Fuxuan Wei, Yijun Liu, Qingfu Zhu, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
| Challenge: | Existing speculative decoding methods require additional model structure and training processes to assist the model for draft token generation. |
| Approach: | They propose a make some noise training framework that introduces some noise at the input for the model to learn the denoising task. |
| Outcome: | The proposed model improves inference speed by 2.3-2.7x times without compromising model performance. |
UniRE: A Unified Label Space for Entity Relation Extraction (2021.acl-long)
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| Challenge: | Existing joint entity relation extraction models setup two separate label spaces for the two sub-tasks . |
| Approach: | They propose to eliminate the different treatment on the two sub-tasks’ label spaces by applying a unified classifier to predict each cell’s label. |
| Outcome: | The proposed model achieves competitive accuracy with the best extractor and is faster. |
RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents (2026.acl-long)
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Jize Wang, Han Wu, Zhiyuan You, Yiming Song, Yijun Wang, Zifei Shan, Yining Li, Songyang Zhang, Xinyi Le, Cailian Chen, Xinping Guan, Dacheng Tao
| Challenge: | Existing methods for mixing-of-agents (MoA) lack model selection criteria and struggle with large model pools. |
| Approach: | They propose a mixture-of-agents framework with dynamic routing that uses a lightweight scorer to perform initial screening and refines the model scores through self- and cross-assessment. |
| Outcome: | The proposed framework outperforms existing methods for large model pools and tasks . it reduces cost by 89.8% and latency by 63.6% in the large-scale model pool. |
Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning (2026.findings-acl)
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Zichuan Fu, Xian Wu, Guojing Li, Yejing Wang, Yijun Chen, Zhao Zihao, Luo Yixuan, Hanyu Yan, Yefeng Zheng, Xiangyu Zhao
| Challenge: | Recent advances in large language models (LLMs) have catalyzed the rise of reasoningintensive inference paradigms, where models perform explicit step-by-step reasoning before generating final answers. |
| Approach: | They propose a large-small LLM collaboration framework that synergizes large and small language models to achieve high-quality reasoning with significantly reduced computational cost. |
| Outcome: | The proposed framework outperforms the mentor LLM while preserving the benefits of the thinking paradigm of LLMs. |
On the Perception Bottleneck of VLMs for Chart Understanding (2025.findings-emnlp)
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| Challenge: | a perception bottleneck in large vision-language models is critical for chart understanding . instruction tuning improves the extraction capability of LVLMs, but the vision encoder remains a bottleneck . |
| Approach: | They propose to decompose the perception bottleneck into two components . the vision encoder bottleneck is where visual representation fails to encapsulate the correct information . |
| Outcome: | The proposed approach significantly mitigates the vision encoder bottleneck and improves the ability of LVLMs to comprehend charts. |
Seer Self-Consistency: Advance Budget Estimation for Adaptive Test-Time Scaling (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional performance in reasoning tasks, particularly in mathematics. |
| Approach: | They propose a dynamic self-consistency framework that integrates System 1 and System 2 reasoning to improve token efficiency and latency. |
| Outcome: | The proposed method outperforms existing methods, achieving up to 47% reduction in token consumption and 43% reduction in inference latency without significant performance loss. |
Encoding and Fusing Semantic Connection and Linguistic Evidence for Implicit Discourse Relation Recognition (2022.findings-acl)
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| Challenge: | Existing studies use one attention mechanism to improve contextual semantic representation learning for implicit discourse relation recognition (IDRR). |
| Approach: | They propose a Multi-Attentive Neural Fusion model to fuse linguistic evidence and semantic connection for IDRR by using a Dual Attention Network and an Offset Matrix Network. |
| Outcome: | The proposed model achieves state-of-the-art on the PDTB 3.0 corpus. |
SALT: Step-level Advantage Assignment for Long-horizon Agents via Trajectory Graph (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities, but their application to complex, multi-step, and long-horizon tasks remains challenging. |
| Approach: | They propose a framework that provides a finer-grained advantage assignment derived solely from outcome rewards. |
| Outcome: | The proposed framework provides a finer-grained advantage assignment, derived solely from outcome rewards. |
Reinforcement Learning for Self-Improving Agent with Skill Library (2026.acl-long)
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Jiongxiao Wang, Qiaojing Yan, Yawei Wang, Yijun Tian, Soumya Smruti Mishra, Zhichao Xu, Megha Gandhi, Panpan Xu, Lin Lee Cheong
| Challenge: | Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in complex reasoning and multi-turn interactions but struggle to continuously improve and adapt when deployed in new environments. |
| Approach: | They propose a Reinforcement Learning-based approach to enhance agents’ self-improvement capabilities with a skill library. |
| Outcome: | The proposed framework achieves 8.9% higher Scenario Goal Completion when applied to supervised-finetuned model with expert experience while requiring 26% fewer interaction steps and generating 59% fewer tokens. |
Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning (2025.findings-acl)
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| Challenge: | Current instruction tuning relies on teacher models or human intervention to generate and refine the instructions and responses for training, which are costly, non-sustainable, and may lack diversity. |
| Approach: | They propose a human/model-free compositional data synthesis method that can create rich and diverse augmentations from existing instruction tuning data to enhance large language models. |
| Outcome: | The proposed method improves performance over benchmarks and reduces training costs by 80% compared with original instruction tuning. |
Improving Grammatical Error Correction via Contextual Data Augmentation (2024.findings-acl)
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| Challenge: | Increasing use of synthetic data due to inconsistent error distribution and noisy labels is limiting the use of these data. |
| Approach: | They propose a method for augmentation of synthetic data with a more consistent error distribution. |
| Outcome: | The proposed method outperforms strong baselines and achieves state-of-the-art with only a few synthetic data. |
Chain-of-Talkers (CoTalk): Fast Human Annotation of Dense Image Captions (2025.emnlp-main)
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| Challenge: | Existing approaches for optimizing human annotation efforts are limited . et al., 2015) suggest that densely annotated image captions improve vision-language alignment . |
| Approach: | They propose an AI-in-the-loop methodology to maximize the number of annotated samples and improve their comprehensiveness under fixed budget constraints. |
| Outcome: | The proposed method improves annotation speed and retrieval performance over the parallel method. |
Adaptive Zooming via Relevance-Informed Positional Resource Allocation for Training-free LLM Context Extension (2026.findings-acl)
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Hongbo Zhao, Huibin Wang, Bin Tang, Xianming Hu, Yihong Huang, Yijun Shen, Nuoyi Chen, Ping Li, Kai Zhang
| Challenge: | Existing training-free methods for extrapolating beyond training context lengths are semantics-agnostic . Existing methods that focus on relative token distances can indiscriminately blur semantically relevant and irrelevant tokens . |
| Approach: | They propose an adaptive positional zooming method that uses semantic relevance to extrapolate beyond training context lengths. |
| Outcome: | Experiments show that RiPRA outperforms existing training-free extrapolation methods . relevant tokens get higher positional resolution, while irrelevant tokens are compressed . |
Adversarial Preference Learning for Robust LLM Alignment (2025.findings-acl)
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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
| 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%. |
LM-Combiner: A Contextual Rewriting Model for Chinese Grammatical Error Correction (2024.lrec-main)
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| Challenge: | Recent work using model ensemble methods based on voting can effectively mitigate over-correction and improve the precision of the GEC system. |
| Approach: | They propose a rewriting model that can directly modify the over-correction of GEC system outputs without a model ensemble. |
| Outcome: | The proposed model can mitigate over-correction and improve accuracy of Chinese grammatical error correction tasks without a model ensemble. |
Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction (2022.emnlp-main)
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| Challenge: | Existing prompt-tuning methods for event argument extraction lack entity information . eAE is a key step of event extraction, but it requires a pre-trained language model to extract event arguments. |
| Approach: | They propose a prompt-tuning method that takes advantage of entity information and pre-trained language models. |
| Outcome: | The proposed method outperforms the state-of-the-art prompt-tuning methods on an english dataset. |
Few Clean Instances Help Denoising Distant Supervision (2022.coling-1)
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| Challenge: | Existing distantly supervised entity relation extractors rely on noisy data for training and evaluation. |
| Approach: | They propose a criterion for clean instance selection based on influence functions to collect sample-level evidence for recognizing good instances. |
| Outcome: | The proposed method shows strong performance on real and synthetic noisy datasets. |
On Hallucination and Predictive Uncertainty in Conditional Language Generation (2021.eacl-main)
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| Challenge: | Modern deep neural network models have brought drastic improvements in generation quality measured by standard metrics on different natural language generation tasks. |
| Approach: | They propose a beam search extension to reduce hallucination in conditional language generation by adding a prediction extension to beam search. |
| Outcome: | The proposed extension improves trading performance on standard metric for less hallucination with the proposed beam search variant. |
ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)
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| Challenge: | Existing methods for joint entity relation extraction use multitask learning frameworks, but annotations for additional tasks are hard to obtain. |
| Approach: | They propose a pre-training method to improve the joint extraction performance with just extra entity annotations. |
| Outcome: | The proposed method outperforms existing methods on ACE05, SciERC, and NYT and outperformed BERT on other tasks. |
Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs (2025.findings-emnlp)
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Yu Yan, Sheng Sun, Zhe Wang, Yijun Lin, Zenghao Duan, Zhifei Zheng, Min Liu, Zhiyi Yin, Jianping Zhang
| Challenge: | Large Language Models have been developed to deal with real-world crimes, but it remains unclear whether they internalize authentic knowledge or are forced to simulate toxic language patterns. |
| Approach: | They construct knowledge-intensive Q&A to investigate misuse threats of Large Language Models in terms of dangerous knowledge possession, harmful task planning utility, and harmfulness judgment robustness. |
| Outcome: | The findings raise concerns that jailbreak success is often attributable to a hallucination loop between jailbroken LLM and judger LLM . |