Generative AI
- Finding Support Examples for In-Context Learning 2024.10.04
- Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator 2024.10.04
- Selective Annotation makes Large Language Models Better Few-shot Learners 2024.10.03
- Large Language Models do In-Context Learning Differently 2024.10.03
- Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations 2024.10.03
- Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? 2024.10.03
- Learning To Retrieve Prompts for In-Context Learning 2024.10.02
- Reordering Examples Helps during Priming-based Few-Shot Learning 2024.10.02
- Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting 2024.10.02
- Calibrate Before Use: Improving Few-shot Performance of Language Models 2024.10.01