Best AI papers explained

527 Episoade

1. Bayesian Scaling Laws for In-Context Learning

   Publicat: 15.05.2025
2. Posterior Mean Matching Generative Modeling

   Publicat: 15.05.2025
3. Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective

   Publicat: 15.05.2025
4. Dynamic Search for Inference-Time Alignment in Diffusion Models

   Publicat: 15.05.2025
5. Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective

   Publicat: 12.05.2025
6. Leaked Claude Sonnet 3.7 System Instruction tuning

   Publicat: 12.05.2025
7. Converging Predictions with Shared Information

   Publicat: 11.05.2025
8. Test-Time Alignment Via Hypothesis Reweighting

   Publicat: 11.05.2025
9. Rethinking Diverse Human Preference Learning through Principal Component Analysis

   Publicat: 11.05.2025
10. Active Statistical Inference

    Publicat: 10.05.2025
11. Data Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework

    Publicat: 10.05.2025
12. AI-Powered Bayesian Inference

    Publicat: 10.05.2025
13. Can Unconfident LLM Annotations Be Used for Confident Conclusions?

    Publicat: 09.05.2025
14. Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI

    Publicat: 09.05.2025
15. Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control

    Publicat: 09.05.2025
16. How to Evaluate Reward Models for RLHF

    Publicat: 09.05.2025
17. LLMs as Judges: Survey of Evaluation Methods

    Publicat: 09.05.2025
18. The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs

    Publicat: 09.05.2025
19. Limits to scalable evaluation at the frontier: LLM as Judge won’t beat twice the data

    Publicat: 09.05.2025
20. Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation

    Publicat: 09.05.2025