From c574d6418302eb166b93cb56b3bb2299d1d5d1b8 Mon Sep 17 00:00:00 2001 From: AtlasApollo Date: Tue, 6 Feb 2024 15:38:20 -0500 Subject: [PATCH] Update examples.en.mdx Fixed some typos --- pages/introduction/examples.en.mdx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pages/introduction/examples.en.mdx b/pages/introduction/examples.en.mdx index 61cfd2e76..0905fdf52 100644 --- a/pages/introduction/examples.en.mdx +++ b/pages/introduction/examples.en.mdx @@ -143,7 +143,7 @@ To highlight why sometimes being specific is important, check out the example be *Prompt:* ``` -Classify the text into nutral, negative or positive. +Classify the text into neutral, negative or positive. Text: I think the vacation is okay. Sentiment: @@ -154,7 +154,7 @@ Sentiment: Neutral ``` -What is the problem here? As a hint, the made-up `nutral` label is completely ignored by the model. Instead, the model outputs `Neutral` as it has some bias towards that label. But let's assume that what you really want is `nutral`. How would you fix this? Maybe you can try adding descriptions to the labels or add more examples to the prompt? If you are not sure, we will discuss a few ideas in the upcoming sections. +What is the problem here? As a hint, the made-up `neutral` label is completely ignored by the model. Instead, the model outputs `Neutral` as it has some bias towards that label. But let's assume that what you really want is `neutral`. How would you fix this? Maybe you can try adding descriptions to the labels or add more examples to the prompt? If you are not sure, we will discuss a few ideas in the upcoming sections. ---