3 papers
cs.LG2026
When and How Unlabeled Data Provably Improve In-Context Learning
Yingcong Li, Xiangyu Chang, Muti Kara +3
Recent research shows that in-context learning (ICL) can be effective even when demonstrations have missing or incorrect labels. To shed light on this capability, we examine a cano…
cs.CV2025
Meta-Entity Driven Triplet Mining for Aligning Medical Vision-Language Models
Saban Ozturk, Melih B. Yilmaz, Muti Kara +3
Diagnostic imaging relies on interpreting both images and radiology reports, but the growing data volumes place significant pressure on medical experts, yielding increased errors a…
cs.CL2025
Provable Benefits of Task-Specific Prompts for In-context Learning
Xiangyu Chang, Yingcong Li, Muti Kara +2
The in-context learning capabilities of modern language models have motivated a deeper mathematical understanding of sequence models. A line of recent work has shown that linear at…