6 papers
On the Pitfalls of Verbalized Confidence Priors for Calibrating Large Reasoning Models
Shuoyuan Wang, Beier Luo, Hao Zeng +5
Large reasoning models (LRMs) often suffer from overconfidence when expressing their uncertainty. Confidence-aware reinforcement learning (RL) offers a promising way to optimize ca…
MarsRetrieval: Benchmarking Vision-Language Models for Planetary-Scale Geospatial Retrieval on Mars
Shuoyuan Wang, Yiran Wang, Hongxin Wei
Data-driven approaches like deep learning are rapidly advancing planetary science, particularly in Mars exploration. Despite recent progress, most existing benchmarks remain confin…
Natural Language-Driven Global Mapping of Martian Landforms
Yiran Wang, Shuoyuan Wang, Zhaoran Wei +7
Planetary surfaces are typically analyzed using high-level semantic concepts in natural language, yet vast orbital image archives remain organized at the pixel level. This mismatch…
Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibrator
Beier Luo, Shuoyuan Wang, Sharon Li +1
Post-training of large language models is essential for adapting pre-trained language models (PLMs) to align with human preferences and downstream tasks. While PLMs typically exhib…
Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models
Shuoyuan Wang, Yixuan Li, Hongxin Wei
Confidence calibration is critical for the safe deployment of machine learning models in the real world. However, such issue in vision-language models like CLIP, particularly after…
Open-Vocabulary Calibration for Fine-tuned CLIP
Shuoyuan Wang, Jindong Wang, Guoqing Wang +3
Vision-language models (VLMs) have emerged as formidable tools, showing their strong capability in handling various open-vocabulary tasks in image recognition, text-driven visual c…