Update README.md

README.md

@@ -62,8 +62,8 @@ allowing for deployment in environments requiring moderated outputs.
 - The author of the [Direct Preference Optimization paper](https://arxiv.org/abs/2305.18290) for the innovative approach
 - The author of the [Pairwise Reward Model for LLMs paper](https://arxiv.org/abs/2306.02561) for the powerful general-purpose reward model
 - The HuggingFace team for the DPO implementation under [The Alignment Handbook](https://github.com/huggingface/alignment-handbook)
-- We would also like to acknowledge contemporary work published on arXiv a few days ago by Meta & NYU (Yuan, et al) in a paper called [Self-Rewarding Language Models](https://arxiv.org/abs/2401.10020), 
-which proposes a similar approach for creating alignment pairs from a larger set of candidate responses, but using the LLM as the reward model.
+- We would also like to acknowledge contemporary work published independently on arXiv on 2024-01-18 by Meta & NYU (Yuan, et al) in a paper called [Self-Rewarding Language Models](https://arxiv.org/abs/2401.10020), 
+which proposes a similar general approach for creating alignment pairs from a larger set of candidate responses, but using the LLM as the reward model.
 While this may work for general-purpose models, our experience has shown that task-specific reward models guided by SMEs are necessary for most 
 enterprise applications of LLMs for specific use cases, which is why we focus on the use of external reward models.