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@@ -100,7 +100,7 @@ People are writing great tools and papers for improving outputs from GPT. Here a
 
 ### Papers on advanced prompting to improve reasoning
 
-- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2022)](https://arxiv.org/abs/2201.11903): Using few-shot prompts to ask models to think step by step improves their reasoning. PaLM's score on math word problems (GSM8K) go from 18% to 57%.
+- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2022)](https://arxiv.org/abs/2201.11903): Using few-shot prompts to ask models to think step by step improves their reasoning. PaLM's score on math word problems (GSM8K) rises from 18% to 57%.
 - [Self-Consistency Improves Chain of Thought Reasoning in Language Models (2022)](https://arxiv.org/abs/2203.11171): Taking votes from multiple outputs improves accuracy even more. Voting across 40 outputs raises PaLM's score on math word problems further, from 57% to 74%, and `code-davinci-002`'s from 60% to 78%.
 - [Tree of Thoughts: Deliberate Problem Solving with Large Language Models (2023)](https://arxiv.org/abs/2305.10601): Searching over trees of step by step reasoning helps even more than voting over chains of thought. It lifts `GPT-4`'s scores on creative writing and crosswords.
 - [Language Models are Zero-Shot Reasoners (2022)](https://arxiv.org/abs/2205.11916): Telling instruction-following models to think step by step improves their reasoning. It lifts `text-davinci-002`'s score on math word problems (GSM8K) from 13% to 41%.
@@ -125,4 +125,4 @@ If there are examples or guides you'd like to see, feel free to suggest them on
 [openai help center]: https://help.openai.com/en/
 [openai examples]: https://beta.openai.com/examples
 [openai blog]: https://openai.com/blog/
-[issues page]: https://github.com/openai/openai-cookbook/issues
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+[issues page]: https://github.com/openai/openai-cookbook/issues