Merge branch 'EleutherAI:main' into main

README.md

@@ -255,7 +255,6 @@ Our primary goal with the Pythia project is to enable research on interpretabili
 
 ## Learning Dynamics
 
-- Biderman, et al. "[Emergent and predictable memorization in large language models.](https://arxiv.org/abs/2304.11158)" _NeurIPS_ (2023).
 - Gupta, et al. "[Continual Pre-Training of Large Language Models: How to re-warm your model?](https://arxiv.org/abs/2308.04014)." _Workshop on Efficient Systems for Foundation Models @ ICML_ (2023).
 - Michaelov and Bergen. "[Emergent inabilities? Inverse scaling over the course of pretraining](https://arxiv.org/abs/2305.14681)." _arXiv preprint arXiv:2305.14681_ (2023).
 - Roger. "[Large Language Models Sometimes Generate Purely Negatively-Reinforced Text](https://arxiv.org/abs/2306.07567)." _arXiv preprint arXiv:2306.07567_ (2023).
@@ -264,6 +263,7 @@ Our primary goal with the Pythia project is to enable research on interpretabili
 
 ## Ethics and Transparency Research
 
+- Biderman, et al. "[Emergent and predictable memorization in large language models.](https://arxiv.org/abs/2304.11158)" _NeurIPS_ (2023).
 - Choi, Shavit, and Duvenaud. "[Tools for Verifying Neural Models' Training Data](https://arxiv.org/abs/2307.00682)." _arXiv preprint arXiv:2307.00682_ (2023).
 - Ippolito, et al. "[Reverse-Engineering Decoding Strategies Given Blackbox Access to a Language Generation System.](https://aclanthology.org/2023.inlg-main.28/)" _Proceedings of the 16th International Natural Language Generation Conference_. 2023.
 - Li et al. "[MoPe: Model Perturbation-based Privacy Attacks on Language Models](https://arxiv.org/abs/2310.14369)." _Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing_ (2023).