From c0ace7c2416a2a58d4213bb00f87a870ba3b6b2d Mon Sep 17 00:00:00 2001 From: Jeremy Zucker Date: Sun, 9 Apr 2023 13:47:10 -0400 Subject: [PATCH] Update ape.en.mdx MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The Automatic Prompt Engineer paper claims that this prompt elicits better results: "Let’s work this out **in** a step by step **way** to be sure we have the right answer." But the prompt in the ape.en.mdx file misquotes the prompt: "Let’s work this out **it** a step by step to be sure we have the right answer." --- pages/techniques/ape.en.mdx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pages/techniques/ape.en.mdx b/pages/techniques/ape.en.mdx index dfe144f..b6086b6 100644 --- a/pages/techniques/ape.en.mdx +++ b/pages/techniques/ape.en.mdx @@ -14,7 +14,7 @@ The first step involves a large language model (as an inference model) that is g APE discovers a better zero-shot CoT prompt than the human engineered "Let's think step by step" prompt ([Kojima et al., 2022](https://arxiv.org/abs/2205.11916)). -The prompt "Let's work this out it a step by step to be sure we have the right answer." elicits chain-of-though reasoning and improves performance on the MultiArith and GSM8K benchmarks: +The prompt "Let’s work this out in a step by step way to be sure we have the right answer." elicits chain-of-though reasoning and improves performance on the MultiArith and GSM8K benchmarks: Image Source: [Zhou et al., (2022)](https://arxiv.org/abs/2211.01910) @@ -23,4 +23,4 @@ This paper touches on an important topic related to prompt engineering which is - [AutoPrompt](https://arxiv.org/abs/2010.15980) - proposes an approach to automatically create prompts for a diverse set of tasks based on gradient-guided search. - [Prefix Tuning](https://arxiv.org/abs/2101.00190) - a lightweight alternative to fine-tuning that prepends a trainable continuous prefix for NLG tasks. -- [Prompt Tuning](https://arxiv.org/abs/2104.08691) - proposes a mechanism for learning soft prompts through backpropagation. \ No newline at end of file +- [Prompt Tuning](https://arxiv.org/abs/2104.08691) - proposes a mechanism for learning soft prompts through backpropagation.