Data: We’ve split the GRT data into three separate and broad categories: factuality, bias, and misdirection. You can only choose ONE dataset to work on per competition.

These are defined as: 

1. Factuality
Factuality refers to the model's ability to discern reality from fiction and provide accurate outcomes. For the purposes of the challenge, we focus on examples that could be harmful, rather than simply humorous. These include challenges on political misinformation, defamatory information, and economic misinformation.

2. Bias
Bias analysis demonstrates and explores model biases. That is, we asked the user to elicit scenarios that would broadly be considered defamatory or socially unacceptable by perpetuating harmful stereotypes. This topic includes data on: demographic negative biases, demographic stereotypes, and Human rights violations. 

3. Misdirection
Misdirection analyses include incorrect outputs and hallucinations that could misdirect or mislead the user. Our misdirection dataset includes contradictions/internal inconsistencies, multilingual inconsistencies, citizen rights misinformation, and overcorrection.

You can find the data here. Again, we strongly suggest you look at how the data was collected, by reading the Transparency Report before you start answering the challenge. Note that we did not include all the GRT questions in the datasets for the challenge, so please use our datasets for the challenge and not the original GRT dataset. 

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