Papers by Jingyu Wang
Erasing Without Remembering: Implicit Knowledge Forgetting in Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | a new method for unlearning large language models is proposed to improve the performance of large language model models. |
| Approach: | They propose a probability perturbation-based unlearning paradigm that allows models to forget implicit knowledge in large language models with a focus on generalisation. |
| Outcome: | The proposed model improves unlearning vanilla target data while forgetting implicit knowledge. |
Rethinking Smoothness for Fast and Adaptable Entity Alignment Decoding (2025.findings-naacl)
Copied to clipboard
| 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. |
VET: Verifiable Execution Tracing for Reliable Text-to-SQL Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for text-to-SQL generation are prone to hallucinations and grounding . authors present a novel reasoning paradigm that transforms text- to-Sql from unverifiable textual rationales into step-wise executable semantics. |
| Approach: | They propose a reasoning paradigm that transforms text-to-SQL from unverifiable textual rationales into step-wise executable semantics. |
| Outcome: | The proposed reasoning paradigm transforms text-to-SQL from unverifiable textual rationales into step-wise executable semantics. |
VALU: A Benchmark for Video Anomaly Temporal Localization and Understanding at Multiple Semantic Levels (2026.acl-long)
Copied to clipboard
Yixiao He, Menghao Zhang, Haifeng Sun, Jing Wang, Kangheng Lin, Jinghan Wang, Chenye Xu, Pengfei Ren, Qi Qi, Jingyu Wang
| Challenge: | Recent advances in Video Large Language Models (Video-LLMs) enhance the ability of VAU models to describe and interpret anomalies. |
| Approach: | They propose a benchmark that explicitly defines anomalies across five semantic levels and provides detailed temporal boundaries and detailed textual descriptions for each. |
| Outcome: | The proposed benchmark defines anomalies across five semantic levels and provides detailed descriptions for each. |
SDiaReward: Modeling and Benchmarking Spoken Dialogue Rewards with Modality and Colloquialness (2026.acl-long)
Copied to clipboard
Jingyu Lu, Yuhan Wang, Fan Zhuo, Xize Cheng, Changhao Pan, Xueyi Pu, Yifu Chen, Chenyuhao Wen, Tianle Liang, Zhou Zhao
| Challenge: | SDiaReward is an end-to-end spoken dialogue system that integrates paralinguistic nuances and spontaneous nature of human conversation. |
| Approach: | They propose an end-to-end multi-turn reward model trained on SDiaReward-Dataset . it is a collection of episode-level preference pairs targeting modality and colloquiality gaps . |
| Outcome: | The proposed model outperforms general-purpose audio LLMs in episode-level evaluation. |
MDR: Model-Specific Demonstration Retrieval at Inference Time for In-Context Learning (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for retrieval-based in-context learning ignore model biases and fail to retrieve the most appropriate demonstrations for different LLMs. |
| Approach: | They propose a model-specific demonstration retrieval method that considers the biases of different LLMs at inference time. |
| Outcome: | The proposed method improves performance on seen and unseen tasks with multi-scale inference LLMs by up to 41.2%. |
ClusterAttn: KV Cache Compression under Intrinsic Attention Clustering (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for sparse attention apply the same pattern across different attention heads and inputs, but fail to capture the intrinsic attention clustering in large language models. |
| Approach: | They propose a training-free sparse attention method that provides an efficient prompt cache compression scheme under intrinsic attention clustering for efficient LLM inference. |
| Outcome: | The proposed method reduces memory usage by 10%–65% and increases throughput by 2.6–4.8 times with no accuracy loss. |
Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to learn complex sentence with multiple aspects do not consider correlation between aspects to distinguish overlapped feature. |
| Approach: | They propose a method that uses aspect correlation to improve aspect correlation modeling . they use Recurrent Mechanism to improve the joint representation of aspects . |
| Outcome: | The proposed method is state-of-the-art in multiaspect scenarios. |
Unveiling Internal Reasoning Modes in LLMs: A Deep Dive into Latent Reasoning vs. Factual Shortcuts with Attribute Rate Ratio (2025.emnlp-main)
Copied to clipboard
Yiran Yang, Haifeng Sun, Jingyu Wang, Qi Qi, Zirui Zhuang, Huazheng Wang, Pengfei Ren, Jing Wang, Jianxin Liao
| Challenge: | Existing research in multi-hop questions has identified two reasoning modes, but has not investigated how these modes differ during inference. |
| Approach: | They propose a classification metric that compares latent reasoning and factual shortcuts in multi-hop questions. |
| Outcome: | The proposed metric achieves 90% accuracy on the proposed datasets and demonstrates effectiveness in RAG conflict scenarios. |
Jailbreak Distillation: Renewable Safety Benchmarking (2025.findings-emnlp)
Copied to clipboard
Jingyu Zhang, Ahmed Elgohary, Xiawei Wang, A S M Iftekhar, Ahmed Magooda, Benjamin Van Durme, Daniel Khashabi, Kyle Jackson
| Challenge: | a new framework for safety benchmarking is being developed for large language models . the framework "distills" jailbreak attacks into high-quality and easily-updatable safety benchmarks . |
| Approach: | They propose a framework that "distills" jailbreak attacks into high-quality safety benchmarks. |
| Outcome: | The proposed framework "distills" jailbreak attacks into high-quality safety benchmarks . it requires minimal human effort to rerun the pipeline and produce updated benchmarks. |
SSS: Editing Factual Knowledge in Language Models towards Semantic Sparse Space (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to modify LMs suffer from sub-optimal locality, where irrelevant neighborhood examples can be adversely influenced. |
| Approach: | They propose to use a model editing method to modify specific examples in LMs to improve locality and reasoning capability by directing the hidden state of edit example towards spaces where semantics are sparse. |
| Outcome: | The proposed method improves locality and reasoning capability on two datasets. |
AutoMonitor-Bench: Evaluating the Reliability of LLM-Based Misbehavior Monitor (2026.findings-acl)
Copied to clipboard
| Challenge: | AutoMonitor-Bench evaluates the reliability of LLM-based misbehavior monitors across diverse tasks and failure modes. |
| Approach: | They introduce AutoMonitor-Bench, a benchmark designed to evaluate misbehavior monitors across diverse tasks and failure modes. |
| Outcome: | The new benchmark evaluates the reliability of LLM-based misbehavior monitors across tasks and failure modes. |
Investigating Capsule Network and Semantic Feature on Hyperplanes for Text Classification (D19-1)
Copied to clipboard
| Challenge: | Various neural networks are designed for text classification on the basis of word embedding, but polysemy is a fundamental feature of the natural language, which brings challenges to text classification. |
| Approach: | They propose to use capsule networks to construct the vectorized representation of semantics and utilize hyperplanes to decompose each capsule to acquire the specific senses. |
| Outcome: | The proposed model extracts more discriminative semantic features and yields significant performance gain compared to baseline methods. |
JARVIS or Ultron? A Survey on the Safety and Security Threats of Computer-Using Agents (2026.acl-long)
Copied to clipboard
Ada Chen, Yongjiang Wu, Junyuan Zhang, Jingyu Xiao, Shu Yang, Jen-tse Huang, Kun Wang, Wenxuan Wang, Shuai Wang
| Challenge: | Recent advances in computer-using agents have created new safety and security risks . despite the impressive capabilities of CUAs, there are still significant security risks. |
| Approach: | They propose a systematization of knowledge on the safety and security threats of Computer-Using Agents. |
| Outcome: | The proposed framework provides a framework for assessing the safety and security risks of computer-using agents. |
Exploring Response Uncertainty in MLLMs: An Empirical Evaluation under Misleading Scenarios (2025.emnlp-main)
Copied to clipboard
Yunkai Dang, Mengxi Gao, Yibo Yan, Xin Zou, Yanggan Gu, Jungang Li, Jingyu Wang, Peijie Jiang, Aiwei Liu, Jia Liu, Xuming Hu
| Challenge: | Existing studies have focused mainly on visual–textual misalignment, leaving largely unexplored the MLLMs’ ability to preserve an original correct answer when confronted with misleading information. |
| Approach: | They propose a two-stage evaluation pipeline to quantify the response uncertainty phenomenon by eliciting each model’s original response on unperturbed inputs and injecting explicit (false-answer hints) and implicit (contextual contradictions) misleading instructions. |
| Outcome: | The proposed model overturns a correct answer in 65% of cases after receiving a single deceptive cue. |
TurkingBench: A Challenge Benchmark for Web Agents (2025.naacl-long)
Copied to clipboard
Kevin Xu, Yeganeh Kordi, Tanay Nayak, Adi Asija, Yizhong Wang, Kate Sanders, Adam Byerly, Jingyu Zhang, Benjamin Van Durme, Daniel Khashabi
| Challenge: | TurkingBench is a benchmark consisting of tasks presented as web pages with textual instructions and multi-modal contexts. |
| Approach: | They propose to use HTML pages to perform various annotation tasks on crowdsourcing platforms. |
| Outcome: | The proposed model outperforms other models on the TurkingBench benchmark. |
Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)
Copied to clipboard
Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao
| Challenge: | Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications. |
| Approach: | They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions. |
| Outcome: | The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions. |
RecStream: Graph-aware Stream Management for Concurrent Recommendation Model Online Serving (2025.coling-industry)
Copied to clipboard
| Challenge: | Existing systems that use recommendation models perform poorly under highly concurrent scenarios. |
| Approach: | They propose a system that optimizes stream configurations based on model characteristics and concurrency levels. |
| Outcome: | The proposed system outperforms existing methods under high concurrency scenarios. |
k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent watermarked generation algorithms inject detectable signatures during language generation to facilitate post-hoc detection. |
| Approach: | They propose a watermark which assigns signatures to each watermarked sentence according to locality-sensitive hashing (LSH) they propose k-SemStamp, which uses kmeans clustering to partition the semantic space with awareness of inherent semantic structure. |
| Outcome: | The proposed watermark improves its robustness and sampling efficiency while preserving the generation quality, making it more effective for machine-generated text detection. |
The Threat of PROMPTS in Large Language Models: A System and User Prompt Perspective (2025.findings-acl)
Copied to clipboard
| Challenge: | Prompts are essential for guiding model output and influencing content generation. |
| Approach: | They propose to attack models with prompt leakage and prompt jailbreak attacks . they summarize the experimental setups of these methods and explore the relationship between prompt threats and prompt injection attacks. |
| Outcome: | The proposed methods summarize the experimental setups and examine the relationship between prompt threats and prompt injection attacks. |
Distantly Supervised Contrastive Learning for Low-Resource Scripting Language Summarization (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for code summarization are limited in resources and require atomic commands and category constraints to enhance code representations. |
| Approach: | They propose a framework that leverages limited atomic commands and category constraints to enhance code representations. |
| Outcome: | The proposed framework outperforms baseline methods in a number of domains and demonstrates superiority over competing frameworks. |
NaviMaster: Learning a Unified Policy for GUI and Embodied Navigation Tasks (2026.acl-long)
Copied to clipboard
| Challenge: | Recent advances in Graphical User Interface (GUI) and embodied navigation have driven progress, yet these domains have largely evolved in isolation, with disparate datasets and training paradigms. |
| Approach: | They propose a visual-target trajectory collection pipeline that generates trajectories for GUI and embodied tasks using a single formulation. |
| Outcome: | The proposed agent outperforms state-of-the-art agents in GUI navigation, spatial affordance prediction, and embodied navigation. |
RATIONALYST: Pre-training Process-Supervision for Improving Reasoning (2025.acl-long)
Copied to clipboard
Dongwei Jiang, Guoxuan Wang, Yining Lu, Andrew Wang, Jingyu Zhang, Chuyu Liu, Benjamin Van Durme, Daniel Khashabi
| Challenge: | RATIONALYST is a model for process-supervision of reasoning based on pretraining on rationale annotations extracted from unlabeled data. |
| Approach: | They propose a model for process-supervision of reasoning based on pre-training on rationale annotations extracted from unlabeled data. |
| Outcome: | RATIONALYST improves reasoning accuracy by 3.9% on representative reasoning benchmarks. |
Effective Long-Context Scaling of Foundation Models (2024.naacl-long)
Copied to clipboard
Wenhan Xiong, Jingyu Liu, Igor Molybog, Hejia Zhang, Prajjwal Bhargava, Rui Hou, Louis Martin, Rashi Rungta, Karthik Abinav Sankararaman, Barlas Oguz, Madian Khabsa, Han Fang, Yashar Mehdad, Sharan Narang, Kshitiz Malik, Angela Fan, Shruti Bhosale, Sergey Edunov, Mike Lewis, Sinong Wang, Hao Ma
| Challenge: | Large language models (LLMs) are rapidly deployed and continue to evolve through scaling. |
| Approach: | They propose a method to train strong long-context LLMs that are capable of utilizing massive context windows of up to 32,000 tokens. |
| Outcome: | The proposed model can surpass gpt-3.5-turbo-16k's overall performance on long-context benchmarks with a cost-effective instruction tuning procedure that is free of expensive annotations. |
On the Blind Spots of Model-Based Evaluation Metrics for Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for text generation evaluation metrics are lacking in robustness analysis. |
| Approach: | They propose to use stress tests to test for errors in text generation evaluation metrics . they find that BERTScore is confused by truncation errors in summarization . |
| Outcome: | The proposed stress tests show that they are insensitive to errors in open-ended generation, translation, and summarization. |
Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis (2020.acl-main)
Copied to clipboard
| Challenge: | Cross-domain sentiment classification requires large amounts of labeled data. |
| Approach: | They propose to apply a pre-training language model BERT on unsupervised domain adaptation . they propose to distill domain-specific features in a self-supervised way . |
| Outcome: | The proposed model outperforms state-of-the-art methods on Amazon dataset . it can be applied to the unsupervised domain adaptation task without domain awareness . |
Text-guided 3D Human Generation from 2D Collections (2023.findings-emnlp)
Copied to clipboard
| Challenge: | 3D human modeling is used for engaging interaction in gaming, film, and animation. however, the customization of characters is crucial for creativity and scalability. |
| Approach: | They propose a 3D human generation using fashion descriptions to enhance 3D geometry transformation and fine-grained consistency. |
| Outcome: | The proposed model can generate a 3D human, guided by a fashion description, with high efficiency. |
SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design (2025.emnlp-main)
Copied to clipboard
Wenxin Tang, Jingyu Xiao, Wenxuan Jiang, Xi Xiao, Yuhang Wang, Xuxin Tang, Qing Li, Yuehe Ma, Junliang Liu, Shisong Tang, Michael R. Lyu
| Challenge: | Existing natural language-based LLM generation methods struggle to capture visual and structural nuances of slide designs. |
| Approach: | They propose a layout-aware framework for generating editable slides from reference images . they propose python code that translates NL instructions into Python code to construct each slide . |
| Outcome: | The proposed framework outperforms state-of-the-art models by up to 40.5 points . it also outperformed open-source models with improved reverse-engineered data. |
Stepwise Reasoning Disruption Attack of LLMs (2025.acl-long)
Copied to clipboard
Jingyu Peng, Maolin Wang, Xiangyu Zhao, Kai Zhang, Wanyu Wang, Pengyue Jia, Qidong Liu, Ruocheng Guo, Qi Liu
| Challenge: | Existing attacks on LLM reasoning are constrained by specific settings or lack of imperceptibility, limiting their feasibility and generalizability. |
| Approach: | They propose a stepwise rEasoning error disruption attack that subtly injects errors into prior reasoning steps to mislead the model into producing incorrect subsequent reasoning and final answers. |
| Outcome: | The proposed attack is compatible with zero-shot and few-shot settings, maintains the natural reasoning flow, and ensures covert execution without modification of the instruction. |
WavAlign: Enhancing Intelligence and Expressiveness in Spoken Dialogue Models via Adaptive Hybrid Post-Training (2026.findings-acl)
Copied to clipboard
Yifu Chen, Shengpeng Ji, Qian Chen, Tianle Liang, Yangzhuo Li, Ziqing Wang, Wen Wang, Jingyu Lu, Haoxiao Wang, Xueyi Pu, Fan Zhuo, Zhou Zhao
| Challenge: | End-to-end spoken dialogue models have higher potential ceiling in expressiveness and perceptual ability than cascaded systems. |
| Approach: | They propose a modality-aware adaptive post-training recipe that constrains preference updates to the semantic channel and improves acoustic behavior via explicit anchoring. |
| Outcome: | The proposed model improves speech quality and expressiveness across spoken dialogue benchmarks and architectures. |
Evaluating and Mitigating Object Hallucination in Large Vision-Language Models: Can They Still See Removed Objects? (2025.naacl-long)
Copied to clipboard
| Challenge: | LVLMs often mistakenly determine objects as present in images where they do not exist . authors propose a new benchmark to evaluate object hallucinations by removing objects from images and asking the model whether it can still see the removed objects. |
| Approach: | They propose a benchmark to evaluate object hallucinations by removing objects from images . they propose oDPO, a direct preference optimization objective based on visual objects . |
| Outcome: | The proposed benchmark reduces the likelihood of object hallucinations by removing objects from images and asking the model whether it can still see the removed objects. |
QueryAttack: Jailbreaking Aligned Large Language Models Using Structured Non-natural Query Language (2025.findings-acl)
Copied to clipboard
Qingsong Zou, Jingyu Xiao, Qing Li, Zhi Yan, Yuhang Wang, Li Xu, Wenxuan Wang, Kuofeng Gao, Ruoyu Li, Yong Jiang
| Challenge: | Existing methods to bypass security defenses of large language models (LLMs) are not effective, but QueryAttack can be jailbroken. |
| Approach: | They propose a framework to examine generalizability of safety alignment by translating malicious queries into structured non-natural query languages. |
| Outcome: | The proposed framework can achieve high attack success rates and jailbreak various defense methods on mainstream LLMs. |
SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation (2024.naacl-long)
Copied to clipboard
Abe Hou, Jingyu Zhang, Tianxing He, Yichen Wang, Yung-Sung Chuang, Hongwei Wang, Lingfeng Shen, Benjamin Van Durme, Daniel Khashabi, Yulia Tsvetkov
| Challenge: | Existing watermarked generation algorithms employ token-level designs and are vulnerable to paraphrase attacks. |
| Approach: | They propose a sentence-level watermarking algorithm that uses locality-sensitive hashing to partition the semantic space of sentences. |
| Outcome: | The proposed algorithm is more robust than the existing state-of-the-art method on paraphrasers and domains, while posing only minor degradations to SemStamp. |
Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expression (2026.findings-acl)
Copied to clipboard
Jingyu Peng, Maolin Wang, Nan Wang, Jiatong Li, Yuchen Li, Yuyang Ye, Wanyu Wang, Pengyue Jia, Kai Zhang, Xiangyu Zhao
| Challenge: | Despite advances in aligning LLMs with human values, current safety mechanisms remain vulnerable to jailbreak attacks. |
| Approach: | They propose a black-box jailbreak method that uses logical expression translation to bypass LLM safety mechanisms. |
| Outcome: | The proposed method exploits the distributional gap between alignment data and logic-expressed inputs while preserving the underlying semantic intent and readability while evading safety constraints. |
Example Quality Matters: Multi-Aspects Example Augmentation for Private Library Programming (2026.acl-long)
Copied to clipboard
Yuhao Li, Haifeng Sun, Xuesong Zhang, Shu Yao, Haoyu Zheng, Yvchuan Wang, Huazheng Wang, Zirui Zhuang, Qi Qi, Jianxin Liao, Jingyu Wang
| Challenge: | Existing approaches to code generation fail to consider the quality of retrieved examples. |
| Approach: | They propose a retrieval-augmented generation method that combines existing API examples to improve complexity and readability. |
| Outcome: | The proposed method achieves up to 22% accuracy improvement over baseline methods. |
Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification (D19-1)
Copied to clipboard
| Challenge: | Existing methods for aspect-level sentiment classification are limited for dealing with overlapped features. |
| Approach: | They propose to use capsule network to construct vector-based feature representation and cluster features by an EM routing algorithm to model semantic relationship between aspect terms and context. |
| Outcome: | The proposed model achieves state-of-the-art on three datasets. |
ModularMoE: Fast LLM Customization with Parameter-Sharing Mixture-of-Experts for Low-Resource Settings (2026.findings-acl)
Copied to clipboard
Jiaxing Liu, Qi Qi, Haifeng Sun, Dunjun Li, Zirui Zhuang, Bo He, Xiang Yang, Cong Liu, Jianxin Liao, Jingyu Wang
| Challenge: | Large Language Models impose significant computational and storage burdens on personal devices . existing customization approaches incur excessive computational costs or lead to suboptimal performance . |
| Approach: | They propose a training framework that converts pre-trained LLMs into parameter-sharing MoE models for lightweight deployment. |
| Outcome: | The proposed training framework outperforms state-of-the-art training frameworks at the same sparsity level while delivering up to 2.71 inference speedup. |