diff --git a/Record/DL/CoT Prompt.md b/Record/DL/CoT Prompt.md
index 6ac9bef..f5989c2 100644
--- a/Record/DL/CoT Prompt.md	
+++ b/Record/DL/CoT Prompt.md	
@@ -1,20 +1,11 @@
 ## Standard Few-shot Prompt
 Prompt: `Q(question) + A(answer)`
-> **Model Input:** Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?
-> A: The answer is 11.
-> Q: The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have?
-> **Model Output:** A: The answer is 27.（x）
-
 ## Few-shot CoT
 思维链提示，就是把一个多步骤推理问题，分解成很多个中间步骤，分配给更多的计算量，生成更多的 token，再把这些答案拼接在一起进行求解。
 Prompt: Q + A(r(rationale) + a(answer))
 Answer: LLM同样会给出理由和答案。
-
-> **Model Input:** Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?
-> A: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11.
-> Q: The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have?
-> **Model Output:** A: The cafeteria had 23 apples originally. They used 20 to make lunch. So they had 23 - 20 = 3. They bought 6 more apples, so they have 3 + 6 = 9. The answer is 9.
-
 ## Zero-shot CoT
 通过 `Let's think step by step` 可以让 LLM 生成回答问题的思维链。我们可以将 Zero-shot CoT 看作一个 pipeline，我们先使用  `Let's think step by step` 让 LLM 尽可能生成一些思考过程，然后将生成的 rationale 和 question 拼接起来，重新配合一个指向 answer 的 Prompt 来激励模型生成答案。
-Prompt: Q + Let's think step by step ｜ LLM | Q + (上一步的输出) + The answer is | LLM ｜ Output
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+Output: (Q + (Q + Let's think step by step ｜ LLM) + The answer is) | LLM
+## Self-consistency
+自洽性，使用多数投票(majority vote)的方式来提升最终回答的准确性。不同于之前的方法，本方法会生成多个思路链，最后取多数答案作为最终答案。
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