From 97eb672e3cbde52972235817ed78706f19edb018 Mon Sep 17 00:00:00 2001 From: bclavie Date: Wed, 5 Apr 2023 17:25:58 +0100 Subject: [PATCH] json formatting + title matching --- pages/applications/_meta.en.json | 6 +++--- pages/applications/workplace_casestudy.en.mdx | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pages/applications/_meta.en.json b/pages/applications/_meta.en.json index 9d47030..8c23b98 100644 --- a/pages/applications/_meta.en.json +++ b/pages/applications/_meta.en.json @@ -1,5 +1,5 @@ { - "pal": "Program-Aided Language Models", - "generating": "Generating Data", - "workplace_casestudy": "Graduate Job Classifcation Case Study" + "pal": "Program-Aided Language Models", + "generating": "Generating Data", + "workplace_casestudy": "Graduate Job Classification Case Study" } diff --git a/pages/applications/workplace_casestudy.en.mdx b/pages/applications/workplace_casestudy.en.mdx index c0012e1..d5297af 100644 --- a/pages/applications/workplace_casestudy.en.mdx +++ b/pages/applications/workplace_casestudy.en.mdx @@ -1,4 +1,4 @@ -# LLMs as Graduate Job Classifiers Case-Study +# Graduate Job Classification Case Study [Clavié et al., 2023](https://arxiv.org/abs/2303.07142) provide a case-study on prompt-engineering applied to a medium-scale text classification use-case in a production system. Using the task of classifying whether a job is a true "entry-level job", suitable for a recent graduate, or not, they evaluated a series of prompt engineering techniques and report their results using GPT-3.5 (`gpt-3.5-turbo`).