Update README.md

README.md

@@ -11,11 +11,11 @@ pinned: false
 
 # Advanced Dual Prompting
 
-This is a playground for testing out Standford's 'Meta-Prompting' logic ([paper link](https://arxiv.org/abs/2401.12954)), in whcih for every user request, it first passes the request to a 'meta' bot, the `meta` bot will generate a system prompt of a field-related 'Expert' bot for answering user's request.  
-That is, for each round, the LLM accordingly assigns the best expert for answering user's specific request.  
+This is a playground for testing out Standford's 'Meta-Prompting' logic ([paper link](https://arxiv.org/abs/2401.12954)), in whcih for every user request, it first passes the request to a 'meta' bot, the 'meta' bot will then generate a system prompt of a field-related 'Expert' bot for answering user's request.  
+That is, for each round, the LLM should accordingly assigns the best expert for answering user's specific request.  
 Standford claimed that this simple implementation result in a 60%+ better accuracy compared to a standard 'syst_prompt + chat_history' logic.  
 Hence, one can't be too curious in checking it out, here is a simple implemnetaion for everybody to play around.  
 
 Something to keep in mind:
 1. Currently it requires an api key from chatglm (get one here if you don't have one: [link](https://open.bigmodel.cn/usercenter/apikeys))
-2. To balanced between contextual-understanding and token-saving, the meta bot's logic is modified to have access to only the last round of chat and the current user request when 'generating' an expert, for now.
+2. To balance contextual-understanding and token-saving, the meta bot's logic is modified to have access to only the last round of chat and the current user request when 'generating' an expert.