Saved in:
| Main Authors: | Wang, Shuoyuan, Li, Yixuan, Wei, Hongxin |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.02681 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibrator
by: Luo, Beier, et al.
Published: (2025)
by: Luo, Beier, et al.
Published: (2025)
Mapping from Meaning: Addressing the Miscalibration of Prompt-Sensitive Language Models
by: Cox, Kyle, et al.
Published: (2025)
by: Cox, Kyle, et al.
Published: (2025)
Open-Vocabulary Calibration for Fine-tuned CLIP
by: Wang, Shuoyuan, et al.
Published: (2024)
by: Wang, Shuoyuan, et al.
Published: (2024)
MarsRetrieval: Benchmarking Vision-Language Models for Planetary-Scale Geospatial Retrieval on Mars
by: Wang, Shuoyuan, et al.
Published: (2026)
by: Wang, Shuoyuan, et al.
Published: (2026)
Large Language Models are Miscalibrated In-Context Learners
by: Li, Chengzu, et al.
Published: (2023)
by: Li, Chengzu, et al.
Published: (2023)
Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition
by: Wang, Shuoyuan, et al.
Published: (2023)
by: Wang, Shuoyuan, et al.
Published: (2023)
How Contaminated Is Your Benchmark? Quantifying Dataset Leakage in Large Language Models with Kernel Divergence
by: Choi, Hyeong Kyu, et al.
Published: (2025)
by: Choi, Hyeong Kyu, et al.
Published: (2025)
Understanding Retrieval-Augmented Task Adaptation for Vision-Language Models
by: Ming, Yifei, et al.
Published: (2024)
by: Ming, Yifei, et al.
Published: (2024)
How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models?
by: Ming, Yifei, et al.
Published: (2023)
by: Ming, Yifei, et al.
Published: (2023)
Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models
by: Liu, Xinyang, et al.
Published: (2023)
by: Liu, Xinyang, et al.
Published: (2023)
Discovery of Hidden Miscalibration Regimes
by: Kobalczyk, Katarzyna, et al.
Published: (2026)
by: Kobalczyk, Katarzyna, et al.
Published: (2026)
Doubly Debiased Test-Time Prompt Tuning for Vision-Language Models
by: Song, Fei, et al.
Published: (2025)
by: Song, Fei, et al.
Published: (2025)
When Individually Calibrated Models Become Collectively Miscalibrated
by: Wang, Zhaohui
Published: (2026)
by: Wang, Zhaohui
Published: (2026)
Learning to Prompt Your Domain for Vision-Language Models
by: Wei, Guoyizhe, et al.
Published: (2023)
by: Wei, Guoyizhe, et al.
Published: (2023)
Provable Joint Decontamination for Benchmarking Multiple Large Language Models
by: Liu, Zhenlong, et al.
Published: (2026)
by: Liu, Zhenlong, et al.
Published: (2026)
Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning
by: Hu, Qiang, et al.
Published: (2024)
by: Hu, Qiang, et al.
Published: (2024)
Provable Model Provenance Set for Large Language Models
by: Qiu, Xiaoqi, et al.
Published: (2026)
by: Qiu, Xiaoqi, et al.
Published: (2026)
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks
by: Chen, Hao, et al.
Published: (2023)
by: Chen, Hao, et al.
Published: (2023)
Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data
by: Zhang, Jiahan, et al.
Published: (2024)
by: Zhang, Jiahan, et al.
Published: (2024)
All Models Are Miscalibrated, But Some Less So: Comparing Calibration with Conditional Mean Operators
by: Moskvichev, Peter, et al.
Published: (2025)
by: Moskvichev, Peter, et al.
Published: (2025)
An Empirical Study of Federated Prompt Learning for Vision Language Model
by: Wang, Zhihao, et al.
Published: (2025)
by: Wang, Zhihao, et al.
Published: (2025)
Neutral-Reference Prompting for Vision-Language Models
by: Tian, Senmao, et al.
Published: (2026)
by: Tian, Senmao, et al.
Published: (2026)
Parametric Scaling Law of Tuning Bias in Conformal Prediction
by: Zeng, Hao, et al.
Published: (2025)
by: Zeng, Hao, et al.
Published: (2025)
CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model
by: Hou, Shihao, et al.
Published: (2025)
by: Hou, Shihao, et al.
Published: (2025)
Provable Training Data Identification for Large Language Models
by: Liu, Zhenlong, et al.
Published: (2025)
by: Liu, Zhenlong, et al.
Published: (2025)
NLPrompt: Noise-Label Prompt Learning for Vision-Language Models
by: Pan, Bikang, et al.
Published: (2024)
by: Pan, Bikang, et al.
Published: (2024)
ZIP: An Efficient Zeroth-order Prompt Tuning for Black-box Vision-Language Models
by: Park, Seonghwan, et al.
Published: (2025)
by: Park, Seonghwan, et al.
Published: (2025)
Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
by: Liu, Zhenlong, et al.
Published: (2024)
by: Liu, Zhenlong, et al.
Published: (2024)
Memory-Space Visual Prompting for Efficient Vision-Language Fine-Tuning
by: Jie, Shibo, et al.
Published: (2024)
by: Jie, Shibo, et al.
Published: (2024)
Latent Domain Prompt Learning for Vision-Language Models
by: Li, Zhixing, et al.
Published: (2025)
by: Li, Zhixing, et al.
Published: (2025)
Understanding Prompt Tuning and In-Context Learning via Meta-Learning
by: Genewein, Tim, et al.
Published: (2025)
by: Genewein, Tim, et al.
Published: (2025)
SecP-Tuning: Efficient Privacy-Preserving Prompt Tuning for Large Language Models via MPC
by: Luo, Jinglong, et al.
Published: (2025)
by: Luo, Jinglong, et al.
Published: (2025)
ADAPT to Robustify Prompt Tuning Vision Transformers
by: Eskandar, Masih, et al.
Published: (2024)
by: Eskandar, Masih, et al.
Published: (2024)
On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbots
by: Herlihy, Christine, et al.
Published: (2024)
by: Herlihy, Christine, et al.
Published: (2024)
R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning
by: Sheng, Lijun, et al.
Published: (2025)
by: Sheng, Lijun, et al.
Published: (2025)
LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language Models
by: Zhang, Yabin, et al.
Published: (2024)
by: Zhang, Yabin, et al.
Published: (2024)
Prompt Tuning for Natural Language to SQL with Embedding Fine-Tuning and RAG
by: Jang, Jisoo, et al.
Published: (2025)
by: Jang, Jisoo, et al.
Published: (2025)
Unlocking the Pre-Trained Model as a Dual-Alignment Calibrator for Post-Trained LLMs
by: Luo, Beier, et al.
Published: (2026)
by: Luo, Beier, et al.
Published: (2026)
A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs
by: Chang, Trenton, et al.
Published: (2025)
by: Chang, Trenton, et al.
Published: (2025)
Mitigate Negative Transfer with Similarity Heuristic Lifelong Prompt Tuning
by: Wu, Chenyuan, et al.
Published: (2024)
by: Wu, Chenyuan, et al.
Published: (2024)
Similar Items
-
Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibrator
by: Luo, Beier, et al.
Published: (2025) -
Mapping from Meaning: Addressing the Miscalibration of Prompt-Sensitive Language Models
by: Cox, Kyle, et al.
Published: (2025) -
Open-Vocabulary Calibration for Fine-tuned CLIP
by: Wang, Shuoyuan, et al.
Published: (2024) -
MarsRetrieval: Benchmarking Vision-Language Models for Planetary-Scale Geospatial Retrieval on Mars
by: Wang, Shuoyuan, et al.
Published: (2026) -
Large Language Models are Miscalibrated In-Context Learners
by: Li, Chengzu, et al.
Published: (2023)