Merge branch 'main' of https://github.com/RUCAIBox/LLMSurvey

Author: EliverQ

README.md

@@ -24,7 +24,7 @@
 
 ## Chinese Version
 
-To facilitate the reading of our (English-verison) survey, we also employ LLMs + some human checking to generate a [**Chinese version**](assets/LLM_Survey__Chinese_V1.pdf) for this survey. While, since it is mainly generated by LLMs, please don't forward or post its content on the Web.
+To facilitate the reading of our (English-verison) survey, we also translate a [**Chinese version**](assets/LLM_Survey_Chinese.pdf) for this survey. We will continue to update the Chinese version.
 
 
 
@@ -663,6 +663,7 @@ Please click [here](Experiments/README.md) to view more detailed information.
 **Attention**
 1. <u>Multi-query attention</u>: **"Fast Transformer Decoding: One Write-Head is All You Need"**. *Noam Shazeer*. arXiv 2019. [[paper](https://arxiv.org/abs/1911.02150)]
 1. <u>FlashAttention</u>: **"FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness"**. *Tri Dao et al*. NeurIPS 2022. [[paper](https://arxiv.org/abs/2205.14135)]
+1. <u>PagedAttention</u>: **"vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention"**. *Woosuk Kwon et al*.  2023.  paper(Stay Tuned) [[Offical WebSite](https://vllm.ai/)]
 
 ##### Analysis
 
@@ -749,7 +750,7 @@ Please click [here](Experiments/README.md) to view more detailed information.
 1. **"Scaling Laws for Reward Model Overoptimization"**. *Leo Gao et al*. arXiv 2022. [[Paper](https://arxiv.org/abs/2210.10760)]
 1. **"The Wisdom of Hindsight Makes Language Models Better Instruction Followers"**. *Tianjun Zhang et al*. arXiv 2023. [[Paper](https://arxiv.org/abs/2302.05206)]
 1. **"RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment"**. *Hanze Dong et al*. arXiv 2023. [[Paper](https://arxiv.org/abs/2304.06767)]
-
+1. **"Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment"**. *Rishabh Bhardwaj et al*. arXiv 2023. [[Paper](https://arxiv.org/abs/2308.09662)]
 
 #### Parameter-Efficient Model Adaptation
 1. **"Parameter-Efficient Transfer Learning for NLP"**. *Neil Houlsby et al*. ICML 2019. [[Paper](https://arxiv.org/abs/1902.00751)] [[GitHub](https://github.com/google-research/adapter-bert)]
@@ -953,6 +954,10 @@ Please click [here](Experiments/README.md) to view more detailed information.
 81. **"Yes but.. Can ChatGPT Identify Entities in Historical Documents?"**. *Carlos-Emiliano González-Gallardo et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2303.17322v1)]
 82. **"Uncovering ChatGPT's Capabilities in Recommender Systems"**. *Sunhao Dai et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2305.02182)]
 83. **"Editing Large Language Models: Problems, Methods, and Opportunities"**. *Yunzhi Yao et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2305.13172)]
+84. **"Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity"**. *Terry Yue Zhuo et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2301.12867)]
+85. **"On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex"**. *Terry Yue Zhuo et al.* EACL 2023. [[Paper](https://arxiv.org/abs/2301.12868)]
+86. **"A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets"**. Laskar et al.* ACL'23. [[Paper]](https://arxiv.org/abs/2305.18486)
+87. **"Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment"**. *Rishabh Bhardwaj et al*. arXiv 2023. [[Paper](https://arxiv.org/abs/2308.09662)]
 
 ### The Team