1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2025★ 1 cited
Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges
Nayoung Lee, Ziyang Cai, Avi Schwarzschild +2
Large language models often struggle with length generalization and solving complex problem instances beyond their training distribution. We present a self-improvement approach whe…
cs.LG2025
Task Vectors in In-Context Learning: Emergence, Formation, and Benefit
Liu Yang, Ziqian Lin, Kangwook Lee +2
In-context learning is a remarkable capability of transformers, referring to their ability to adapt to specific tasks based on a short history or context. Previous research has fou…