Multicast Capacity With Max-Min Fairness for Heterogeneous Networks. - Yixuan Li 001; Qiuyu Peng; Xinbing Wang (IEEE/ACM Trans. Netw., 2014)

Detecting Overlapping Communities from Local Spectral Subspaces. - Kun He 001; Yiwei Sun; David Bindel; John E. Hopcroft; Yixuan Li 001 (ICDM, 2015)

Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach. - Yixuan Li 001; Kun He 001; David Bindel; John E. Hopcroft (WWW, 2015)

Deep Manifold Traversal: Changing Labels with Convolutional Features. - Jacob R. Gardner; Matt J. Kusner; Yixuan Li 001; Paul Upchurch; Kilian Q. Weinberger; John E. Hopcroft (CoRR, 2015)

Overlapping Community Detection via Local Spectral Clustering. - Yixuan Li 001; Kun He 001; David Bindel; John E. Hopcroft (CoRR, 2015)

The Lifecycle and Cascade of Social Messaging Groups. - Jiezhong Qiu; Yixuan Li 001; Jie Tang 001; Zheng Lu; Hao Ye; Bo Chen 026; Qiang Yang 001; John E. Hopcroft (CoRR, 2015)

On Multicast Capacity and Delay in Cognitive Radio Mobile Ad Hoc Networks. - Jinbei Zhang; Yixuan Li 001; Zhuotao Liu; Fan Wu 006; Feng Yang 006; Xinbing Wang (IEEE Trans. Wirel. Commun., 2015)

In a World That Counts: Clustering and Detecting Fake Social Engagement at Scale. - Yixuan Li 001; Oscar Martinez; Xing Chen; Yi Li; John E. Hopcroft (WWW, 2016)

The Lifecycle and Cascade of WeChat Social Messaging Groups. - Jiezhong Qiu; Yixuan Li 001; Jie Tang 001; Zheng Lu; Hao Ye; Bo Chen 026; Qiang Yang 001; John E. Hopcroft (WWW, 2016)

Scalable and Robust Local Community Detection via Adaptive Subgraph Extraction and Diffusions. - Kyle Kloster; Yixuan Li 001 (CoRR, 2016)

Convergent Learning: Do different neural networks learn the same representations? - Yixuan Li 001; Jason Yosinski; Jeff Clune; Hod Lipson; John E. Hopcroft (ICLR, 2016)

Stacked Generative Adversarial Networks. - Xun Huang; Yixuan Li 001; Omid Poursaeed; John E. Hopcroft; Serge J. Belongie (CVPR, 2017)

Snapshot Ensembles: Train 1, Get M for Free. - Gao Huang 001; Yixuan Li 001; Geoff Pleiss; Zhuang Liu 003; John E. Hopcroft; Kilian Q. Weinberger (ICLR (Poster), 2017)

Towards Measuring and Inferring User Interest from Gaze. - Yixuan Li 001; Pingmei Xu; Dmitry Lagun; Vidhya Navalpakkam (WWW (Companion Volume), 2017)

Principled Detection of Out-of-Distribution Examples in Neural Networks. - Shiyu Liang; Yixuan Li 001; R. Srikant 001 (CoRR, 2017)

Exploring the Limits of Weakly Supervised Pretraining. - Dhruv Mahajan 001; Ross B. Girshick; Vignesh Ramanathan; Kaiming He; Manohar Paluri; Yixuan Li 001; Ashwin Bharambe; Laurens van der Maaten (ECCV (2), 2018)

Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks. - Shiyu Liang; Yixuan Li 001; R. Srikant 001 (ICLR (Poster), 2018)

Understanding the Loss Surface of Single-Layered Neural Networks for Binary Classification. - Shiyu Liang; Ruoyu Sun 001; Yixuan Li 001; R. Srikant 001 (ICLR (Workshop), 2018)

Understanding the Loss Surface of Neural Networks for Binary Classification. - Shiyu Liang; Ruoyu Sun 001; Yixuan Li 001; Rayadurgam Srikant (ICML, 2018)

Local Spectral Clustering for Overlapping Community Detection. - Yixuan Li 001; Kun He 001; Kyle Kloster; David Bindel; John E. Hopcroft (ACM Trans. Knowl. Discov. Data, 2018)

Defense Against Adversarial Images Using Web-Scale Nearest-Neighbor Search. - Abhimanyu Dubey; Laurens van der Maaten; Zeki Yalniz; Yixuan Li 001; Dhruv Mahajan 001 (CVPR, 2019)

MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data. - Yina Tang; Fedor Borisyuk; Siddarth Malreddy; Yixuan Li 001; Yiqun Liu 006; Sergey Kirshner (KDD, 2019)

Energy-based Out-of-distribution Detection. - Weitang Liu; Xiaoyun Wang; John D. Owens; Yixuan Li 001 (NeurIPS, 2020)

Robust Out-of-distribution Detection in Neural Networks. - Jiefeng Chen 001; Yixuan Li 001; Xi Wu 001; Yingyu Liang; Somesh Jha (CoRR, 2020)

Robust Out-of-distribution Detection via Informative Outlier Mining. - Jiefeng Chen 001; Yixuan Li 001; Xi Wu 001; Yingyu Liang; Somesh Jha (CoRR, 2020)

MOS: Towards Scaling Out-of-Distribution Detection for Large Semantic Space. - Rui Huang; Yixuan Li 001 (CVPR, 2021)

Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving. - Mu Cai; Hong Zhang 009; Huijuan Huang; Qichuan Geng; Yixuan Li 001; Gao Huang 001 (ICCV, 2021)

Model Patching: Closing the Subgroup Performance Gap with Data Augmentation. - Karan Goel; Albert Gu; Sharon Li 001; Christopher Ré (ICLR, 2021)

On the Importance of Gradients for Detecting Distributional Shifts in the Wild. - Rui Huang; Andrew Geng; Yixuan Li 001 (NeurIPS, 2021)

ReAct: Out-of-distribution Detection With Rectified Activations. - Yiyou Sun; Chuan Guo 001; Yixuan Li 001 (NeurIPS, 2021)

Can multi-label classification networks know what they don't know? - Haoran Wang; Weitang Liu; Alex Bocchieri; Yixuan Li 001 (NeurIPS, 2021)

ATOM: Robustifying Out-of-Distribution Detection Using Outlier Mining. - Jiefeng Chen 001; Yixuan Li 001; Xi Wu 001; Yingyu Liang; Somesh Jha (ECML/PKDD (3), 2021)

Natural Attribute-based Shift Detection. - Jeonghoon Park; Jimin Hong; Radhika Dua; Daehoon Gwak; Yixuan Li 001; Jaegul Choo; Edward Choi (CoRR, 2021)

On the Effectiveness of Sparsification for Detecting the Deep Unknowns. - Yiyou Sun; Yixuan Li 001 (CoRR, 2021)

On the Impact of Spurious Correlation for Out-of-Distribution Detection. - Yifei Ming; Hang Yin; Yixuan Li 001 (AAAI, 2022)

Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild. - Xuefeng Du; Xin Wang 066; Gabriel Gozum; Yixuan Li 001 (CVPR, 2022)

DICE: Leveraging Sparsification for Out-of-Distribution Detection. - Yiyou Sun; Yixuan Li 001 (ECCV (24), 2022)

VOS: Learning What You Don't Know by Virtual Outlier Synthesis. - Xuefeng Du; Zhaoning Wang; Mu Cai; Yixuan Li 001 (ICLR, 2022)

PiCO: Contrastive Label Disambiguation for Partial Label Learning. - Haobo Wang 001; Ruixuan Xiao; Yixuan Li 001; Lei Feng 006; Gang Niu 001; Gang Chen 001; Junbo Zhao 002 (ICLR, 2022)

Training OOD Detectors in their Natural Habitats. - Julian Katz-Samuels; Julia B. Nakhleh; Robert D. Nowak; Yixuan Li 001 (ICML, 2022)

POEM: Out-of-Distribution Detection with Posterior Sampling. - Yifei Ming; Ying Fan; Yixuan Li 001 (ICML, 2022)

Out-of-Distribution Detection with Deep Nearest Neighbors. - Yiyou Sun; Yifei Ming; Xiaojin Zhu 001; Yixuan Li 001 (ICML, 2022)

Mitigating Neural Network Overconfidence with Logit Normalization. - Hongxin Wei; Renchunzi Xie; Hao Cheng 014; Lei Feng 006; Bo An 001; Yixuan Li 001 (ICML, 2022)

SIREN: Shaping Representations for Detecting Out-of-Distribution Objects. - Xuefeng Du; Gabriel Gozum; Yifei Ming; Yixuan Li 001 (NeurIPS, 2022)

Is Out-of-Distribution Detection Learnable? - Zhen Fang 001; Yixuan Li 001; Jie Lu 001; Jiahua Dong 001; Bo Han 003; Feng Liu 003 (NeurIPS, 2022)

Delving into Out-of-Distribution Detection with Vision-Language Representations. - Yifei Ming; Ziyang Cai; Jiuxiang Gu; Yiyou Sun; Wei Li; Yixuan Li 001 (NeurIPS, 2022)

SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning. - Haobo Wang 001; Mingxuan Xia; Yixuan Li 001; Yuren Mao; Lei Feng 006; Gang Chen 001; Junbo Zhao 002 (NeurIPS, 2022)

OpenOOD: Benchmarking Generalized Out-of-Distribution Detection. - Jingkang Yang; Pengyun Wang; Dejian Zou; Zitang Zhou; Kunyuan Ding; Wenxuan Peng; Haoqi Wang; Guangyao Chen; Bo Li 080; Yiyou Sun; Xuefeng Du; Kaiyang Zhou; et al. (NeurIPS, 2022)

CIDER: Exploiting Hyperspherical Embeddings for Out-of-Distribution Detection. - Yifei Ming; Yiyou Sun; Ousmane Dia; Yixuan Li 001 (CoRR, 2022)

Are Vision Transformers Robust to Spurious Correlations? - Soumya Suvra Ghosal; Yifei Ming; Yixuan Li 001 (CoRR, 2022)

Open-world Contrastive Learning. - Yiyou Sun; Yixuan Li 001 (CoRR, 2022)

Logit Clipping for Robust Learning against Label Noise. - Hongxin Wei; Huiping Zhuang; Renchunzi Xie; Lei Feng 006; Gang Niu 001; Bo An 001; Yixuan Li 001 (CoRR, 2022)

A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges. - Mohammadreza Salehi; Hossein Mirzaei; Dan Hendrycks; Yixuan Li 001; Mohammad Hossein Rohban; Mohammad Sabokrou (Trans. Mach. Learn. Res., 2022)

Distributionally Robust Optimization with Probabilistic Group. - Soumya Suvra Ghosal; Yixuan Li 001 (AAAI, 2023)

Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection. - Rheeya Uppaal; Junjie Hu 001; Yixuan Li 001 (ACL (1), 2023)

Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment. - Yiyou Sun; Yaojie Liu; Xiaoming Liu; Yixuan Li 001; Wen-Sheng Chu (CVPR, 2023)

A Critical Analysis of Document Out-of-Distribution Detection. - Jiuxiang Gu; Yifei Ming; Yi Zhou; Jason Kuen; Vlad I. Morariu; Handong Zhao; Ruiyi Zhang 002; Nikolaos Barmpalios; Anqi Liu; Yixuan Li 001; Tong Sun 005; Ani Nenkova (EMNLP (Findings), 2023)

How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection? - Yifei Ming; Yiyou Sun; Ousmane Dia; Yixuan Li 001 (ICLR, 2023)

Non-parametric Outlier Synthesis. - Leitian Tao; Xuefeng Du; Jerry Zhu; Yixuan Li 001 (ICLR, 2023)

Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection. - Haoyue Bai 001; Gregory Canal; Xuefeng Du; Jeongyeol Kwon; Robert D. Nowak; Yixuan Li 001 (ICML, 2023)

When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis. - Yiyou Sun; Zhenmei Shi; Yingyu Liang; Yixuan Li 001 (ICML, 2023)

Mitigating Memorization of Noisy Labels by Clipping the Model Prediction. - Hongxin Wei; Huiping Zhuang; Renchunzi Xie; Lei Feng 006; Gang Niu 001; Bo An 001; Yixuan Li 001 (ICML, 2023)

Dream the Impossible: Outlier Imagination with Diffusion Models. - Xuefeng Du; Yiyou Sun; Jerry Zhu; Yixuan Li 001 (NeurIPS, 2023)

A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning. - Yiyou Sun; Zhenmei Shi; Yixuan Li 001 (NeurIPS, 2023)

Learning to Augment Distributions for Out-of-distribution Detection. - Qizhou Wang; Zhen Fang 001; Yonggang Zhang 003; Feng Liu 003; Yixuan Li 001; Bo Han 003 (NeurIPS, 2023)

Designing Fair AI Systems: Exploring the Interaction of Explainable AI and Task Objectivity on Users' Fairness Perception. - Tingru Cui; Sharon Li 001; Kaiping Chen; James Bailey 001; Feng Liu 003 (PACIS, 2023)

Out-of-distribution Detection via Frequency-regularized Generative Models. - Mu Cai; Yixuan Li 001 (WACV, 2023)

Task Agnostic and Post-hoc Unseen Distribution Detection. - Radhika Dua; Seongjun Yang; Yixuan Li 001; Edward Choi (WACV, 2023)

Non-Parametric Outlier Synthesis. - Leitian Tao; Xuefeng Du; Xiaojin Zhu 001; Yixuan Li 001 (CoRR, 2023)

OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection. - Jingyang Zhang; Jingkang Yang; Pengyun Wang; Haoqi Wang; Yueqian Lin; Haoran Zhang; Yiyou Sun; Xuefeng Du; Kaiyang Zhou; Wayne Zhang 001; Yixuan Li 001; Ziwei Liu 002; et al. (CoRR, 2023)

Dream the Impossible: Outlier Imagination with Diffusion Models. - Xuefeng Du; Yiyou Sun; Xiaojin Zhu 001; Yixuan Li 001 (CoRR, 2023)

Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions. - Xuefeng Du; Tian Bian; Yu Rong 001; Bo Han 003; Tongliang Liu; Tingyang Xu; Wenbing Huang 001; Yixuan Li 001; Junzhou Huang (Trans. Mach. Learn. Res., 2023)

OpenCon: Open-world Contrastive Learning. - Yiyou Sun; Yixuan Li 001 (Trans. Mach. Learn. Res., 2023)

How to Overcome Curse-of-Dimensionality for Out-of-Distribution Detection? - Soumya Suvra Ghosal; Yiyou Sun; Yixuan Li 001 (AAAI, 2024)

Targeted Representation Alignment for Open-World Semi-Supervised Learning. - Ruixuan Xiao; Lei Feng 006; Kai Tang; Junbo Zhao 002; Yixuan Li 001; Gang Chen 001; Haobo Wang 001 (CVPR, 2024)

HYPO: Hyperspherical Out-Of-Distribution Generalization. - Haoyue Bai 001; Yifei Ming; Julian Katz-Samuels; Yixuan Li 001 (ICLR, 2024)

How Does Unlabeled Data Provably Help Out-of-Distribution Detection? - Xuefeng Du; Zhen Fang 001; Ilias Diakonikolas; Yixuan Li 001 (ICLR, 2024)

ARGS: Alignment as Reward-Guided Search. - Maxim Khanov; Jirayu Burapacheep; Yixuan Li 001 (ICLR, 2024)

ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection. - Bo Peng; Yadan Luo; Yonggang Zhang 003; Yixuan Li 001; Zhen Fang 001 (ICLR, 2024)

When and How Does In-Distribution Label Help Out-of-Distribution Detection? - Xuefeng Du; Yiyou Sun; Yixuan Li 001 (ICML, 2024)

Understanding the Learning Dynamics of Alignment with Human Feedback. - Shawn Im; Yixuan Li 001 (ICML, 2024)

Understanding Retrieval-Augmented Task Adaptation for Vision-Language Models. - Yifei Ming; Yixuan Li 001 (ICML, 2024)

HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detection. - Xuefeng Du; Chaowei Xiao; Sharon Li 001 (NeurIPS, 2024)

Bridging OOD Detection and Generalization: A Graph-Theoretic View. - Han Wang 019; Sharon Li 001 (NeurIPS, 2024)

BackdoorAlign: Mitigating Fine-tuning based Jailbreak Attack with Backdoor Enhanced Safety Alignment. - Jiongxiao Wang; Jiazhao Li; Yiquan Li; Xiangyu Qi; Junjie Hu 001; Sharon Li 001; Patrick McDaniel; Muhao Chen 001; Bo Li 026; Chaowei Xiao (NeurIPS, 2024)

Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models. - Jiayu Wang; Yifei Ming; Zhenmei Shi; Vibhav Vineet; Xin Wang 066; Sharon Li 001; Neel Joshi (NeurIPS, 2024)

DCAI: Data-centric Artificial Intelligence. - Wei Jin 009; Haohan Wang; Daochen Zha; Qiaoyu Tan; Yao Ma 001; Sharon Li 001; Su-In Lee (WWW (Companion Volume), 2024)

FEUDA: Frustratingly Easy Prompt Based Unsupervised Domain Adaptation. - Rheeya Uppaal; Yixuan Li 001; Junjie Hu 001 (CoRR, 2024)

Mitigating Fine-tuning Jailbreak Attack with Backdoor Enhanced Alignment. - Jiongxiao Wang; Jiazhao Li; Yiquan Li; Xiangyu Qi; Junjie Hu 001; Yixuan Li 001; Patrick McDaniel; Muhao Chen 001; Bo Li 026; Chaowei Xiao (CoRR, 2024)

Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models. - Atsuyuki Miyai; Jingkang Yang; Jingyang Zhang; Yifei Ming; Qing Yu; Go Irie; Yixuan Li 001; Hai Li 001; Ziwei Liu 002; Kiyoharu Aizawa (CoRR, 2024)

On the Generalization of Preference Learning with DPO. - Shawn Im; Yixuan Li 001 (CoRR, 2024)

Bridging OOD Detection and Generalization: A Graph-Theoretic View. - Han Wang; Yixuan Li 001 (CoRR, 2024)

VLMGuard: Defending VLMs against Malicious Prompts via Unlabeled Data. - Xuefeng Du; Reshmi Ghosh; Robert Sim; Ahmed Salem; Vitor Carvalho; Emily Lawton; Yixuan Li 001; Jack W. Stokes (CoRR, 2024)

How Reliable Is Human Feedback For Aligning Large Language Models? - Min-Hsuan Yeh; Leitian Tao; Jeffrey Wang; Xuefeng Du; Yixuan Li 001 (CoRR, 2024)

Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models. - Shuoyuan Wang; Yixuan Li 001; Hongxin Wei (CoRR, 2024)

Safety-Aware Fine-Tuning of Large Language Models. - Hyeong Kyu Choi; Xuefeng Du; Yixuan Li 001 (CoRR, 2024)

Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts. - Jihye Choi; Jayaram Raghuram; Yixuan Li 001; Somesh Jha (CoRR, 2024)

Are Vision Transformers Robust to Spurious Correlations? - Soumya Suvra Ghosal; Yixuan Li 001 (Int. J. Comput. Vis., 2024)

On the Learnability of Out-of-distribution Detection. - Zhen Fang 001; Yixuan Li 001; Feng Liu 003; Bo Han 003; Jie Lu 001 (J. Mach. Learn. Res., 2024)

Your Classifier Can Be Secretly a Likelihood-Based OOD Detector. - Jirayu Burapacheep; Yixuan Li 001 (Trans. Mach. Learn. Res., 2024)

How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models? - Yifei Ming; Yixuan Li 001 (Int. J. Comput. Vis., 2024)

PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning. - Haobo Wang 001; Ruixuan Xiao; Yixuan Li 001; Lei Feng 006; Gang Niu 001; Gang Chen 001; Junbo Zhao 002 (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Generalized Out-of-Distribution Detection: A Survey. - Jingkang Yang; Kaiyang Zhou; Yixuan Li 001; Ziwei Liu 002 (Int. J. Comput. Vis., 2024)

Unsolvable Problem Detection: Robust Understanding Evaluation for Large Multimodal Models. - Atsuyuki Miyai; Jingkang Yang; Jingyang Zhang; Yifei Ming; Qing Yu; Go Irie; Yixuan Li 001; Hai Helen Li; Ziwei Liu 002; Kiyoharu Aizawa (ACL (1), 2025)

CONDA: Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts. - Jihye Choi; Jayaram Raghuram; Yixuan Li 001; Somesh Jha (ICLR, 2025)

Process Reward Model with Q-value Rankings. - Wendi Li; Yixuan Li 001 (ICLR, 2025)

DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation. - Changdae Oh; Yixuan Li 001; Kyungwoo Song; Sangdoo Yun; Dongyoon Han (ICLR, 2025)

Your Weak LLM is Secretly a Strong Teacher for Alignment. - Leitian Tao; Yixuan Li 001 (ICLR, 2025)

Out-of-Distribution Learning with Human Feedback. - Haoyue Bai 001; Xuefeng Du; Katie Rainey; Shibin Parameswaran; Yixuan Li 001 (Trans. Mach. Learn. Res., 2025)

Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey. - Atsuyuki Miyai; Jingkang Yang; Jingyang Zhang; Yifei Ming; Yueqian Lin; Qing Yu; Go Irie; Shafiq Joty; Yixuan Li 001; Hai Helen Li; Ziwei Liu 002; Toshihiko Yamasaki; et al. (Trans. Mach. Learn. Res., 2025)

CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement. - Leitian Tao; Xiang Chen; Tong Yu 001; Tung Mai; Ryan A. Rossi; Yixuan Li 001; Saayan Mitra (Trans. Mach. Learn. Res., 2025)

Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach. - Changdae Oh; Zhen Fang 001; Shawn Im; Xuefeng Du; Yixuan Li 001 (CoRR, 2025)

How Contaminated Is Your Benchmark? Quantifying Dataset Leakage in Large Language Models with Kernel Divergence. - Hyeong Kyu Choi; Maxim Khanov; Hongxin Wei; Yixuan Li 001 (CoRR, 2025)

A Unified Understanding and Evaluation of Steering Methods. - Shawn Im; Yixuan Li 001 (CoRR, 2025)

Membership Inference Attack Should Move On to Distributional Statistics for Distilled Generative Models. - Muxing Li; Zesheng Ye; Yixuan Li 001; Andy Song; Guangquan Zhang 001; Feng Liu 003 (CoRR, 2025)

Can Your Uncertainty Scores Detect Hallucinated Entity? - Min-Hsuan Yeh; Max Kamachee; Seongheon Park; Yixuan Li 001 (CoRR, 2025)

Category-free Out-of-Distribution Node Detection with Feature Resonance. - Shenzhi Yang; Junbo Zhao 002; Shouqing Yang; Yixuan Li 001; Dingyu Yang; Xiaofang Zhang; Haobo Wang 001 (CoRR, 2025)

How to Steer LLM Latents for Hallucination Detection? - Seongheon Park; Xuefeng Du; Min-Hsuan Yeh; Haobo Wang 001; Yixuan Li 001 (CoRR, 2025)

Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibrator. - Beier Luo; Shuoyuan Wang; Yixuan Li 001; Hongxin Wei (CoRR, 2025)