ΑΙhub.org
 

GPT-3 in tweets

by
26 August 2020



share this:

AIhub | Tweets round-up
Since OpenAI released GPT-3, you have probably come across examples of impressive and/or problematic content that people have used the model to generate. Here we summarise the outputs of GPT-3 as seen through the eyes of the Twitter-sphere.

GPT-3 is able to generate impressive examples, such as these.

However, caution is needed when using the model. Although it can produce good results, it is important to be aware of the limitations of such a system.

GPT-3 has been shown to replicate offensive and harmful phrases and concepts, like the examples presented in the following tweets.

This harmful concept generation is not limited to English.

It is important to note that GPT-2 had similar problems. This EMNLP paper by Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng pointed out the issue.

GPT-3 should indeed be used with caution.

 




Nedjma Ousidhoum is a postdoc at the University of Cambridge.
Nedjma Ousidhoum is a postdoc at the University of Cambridge.




            AIhub is supported by:


Related posts :



AIhub monthly digest: January 2023 – low-resource language projects, Earth’s nightlights and a Lanfrica milestone

Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.
31 January 2023, by

The Good Robot Podcast: featuring Abeba Birhane

In this episode, Eleanor and Kerry talk to Abeba Birhane about changing computing cultures.
30 January 2023, by

All questions answered: how CLAIRE shapes the future of AI in Europe

Watch the next in the series of CLAIRE's All Questions Answered (AQuA) events.
27 January 2023, by

UrbanTwin: seeing double for sustainability

A digital twin for urban infrastructure: assessing the effectiveness of climate-related policies and actions.
26 January 2023, by

Counterfactual explanations for land cover mapping: interview with Cassio Dantas

Cassio tells us about work applying counterfactual explanations to remote sensing time series data for land-cover mapping classification.
25 January 2023, by





©2021 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association