Update README.md

README.md

@@ -18,6 +18,7 @@ This is the official repository for "[GPTFUZZER: Red Teaming Large Language Mode
 - [FQA](#fqa)
 
 ## Updates
+- (2023/10/25) 🏆 🏆 We won the Frontier Breakthrough Award and Outstanding Presentation Award at Geekcon 2023!
 - (2023/10/22) Our work will be presented in the [Geekcon 2023](https://geekcon.darknavy.com/2023/china/en/index.html) conference! We will give a talk about our work and give a live attack demo with our tool. We will also include the attack results for Chinese LLMs which have not be included in our paper. We are looking forward to meeting you there!
 - (2023/10/21) We have updated our codebase to make it more readable and easier to use. We will continue to update the codebase and add more features and other implementations. We are looking forward to build a general black-box fuzzing framework for large language models. Stay tuned!
 - (2023/9/19) Our paper is on arXiv! Check it out [here](https://arxiv.org/pdf/2309.10253.pdf)!
@@ -74,4 +75,4 @@ Due to ethical concern, we decided not to release the adversarial templates we f
     - We found that use batched inference can significantly speed up the fuzzing process. However, the results might be slightly different from the original results because of the padding tokens (see [here](https://github.com/tloen/alpaca-lora/issues/20)). We suggest using [vllm](https://github.com/vllm-project/vllm) inference for hyper performance
 3. How could I implement my own mutator/seed selector?
     - You can implement your own mutator/seed selector by inheriting the class. You can refer to `mutator.py` and `selection.py` for examples.
-    Also, as we claimed, we would like to work on a general black-box fuzzing framework for large language models. If you have some ideas or suggestions or you find other papers that are related to this topic, please let us know or leave the comment in the issue. We are happy to implement them and make this framework more powerful.
+    Also, as we claimed, we would like to work on a general black-box fuzzing framework for large language models. If you have some ideas or suggestions or you find other papers that are related to this topic, please let us know or leave the comment in the issue. We are happy to implement them and make this framework more powerful.