task_categories:text-classification language:en size_categories:10K<n<100K region:us climate
---
task_categories:
- text-classification
language:
- en
tags:
- climate
size_categories:
- 10K<n<100K
---
## Frugal-AI-Train-Data-88k
 
Synthetic and Real Data combined for the training of the models submitted for the Frugal AI challenge text part of the competition.
 
**Composition of the Dataset**
 
The dataset contains samples from various sources :
Synthetic Quotes : Around 75k of examples from various models, generated for all labels.
Relabelled additional examples from real sources : Around 10k additional examples that were labelled both manually and using GPT-4o with examples. Data was extracted from DESMOG, the CARDS dataset and scrapped from the Skeptical Science website.
This set was used alongside both the Training and Test sets of the competition.
 
**Sources of Synthetic Data**
 
The synthetic data was generated using the following models :
Gemini 2.0 Flash Thinking
Claude 3.5 Sonnet
GPT-4o
o1-preview
Mistral Large
Groq-Llama-3.1-70B
Prompts
 
The prompts that were used were the following :

Rewriting Prompt : A prompt that rewrites the data to create new samples. We noticed different models yielded different examples, which allowed for more diversity.

Answering Prompt : A prompt on which different models give an answer to climate disinformation argument, generating "Not relevant" examples. The idea was to avoid the models cheating the classification task using words or arguments they see spun one way around.

Attempts that didn't make the cut to establish sample diversity

Forcing first words : Samples stopped being diverse after 100 generations.

Imposing a place restriction : Telling the model the person was speaking from "a location" allowed generations that could take into account location-specific arguments related to climate science distrust. However, the models did not manage to make good work out of this (at the exception of Claude 3.5 Sonnet or o1 on our first tests). Hence why we swapped to personas, which were much more qualitative regarding information.

Generating from Personas : We generated 50 different personas using o1-preview, that are people experiencing climate science distrust for various reasons. However, no improvement in training resulted from their use. While we believe they can be interesting as a whole, they were not in the case of this competition.

All models used were trained using this dataset.

Through the open-sourcing of this dataset, we aim to improve open research on the topic. The mixture of real and synthetic data could also improve open research on another topic, related to the detection of artificially generated data.