K2P: Label-Free Knowledge to Prompt Distillation
cs.LG, cs.CL
Submitted: 2026-09-30
Updated: 2026-09-30
Code: https://github.com/google/BIG-bench
Terminology
Sources
- GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
- Distilling the Knowledge in a Neural Network
- Large Language Models Cannot Self-Correct Reasoning Yet
- ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory
- Large Language Models as Optimizers
- Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
- Large Language Models Are Human-Level Prompt Engineers
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks