Name: LEANDRO FURLAM TURI
Publication date: 02/12/2024
Examining board:
Name |
Role |
|---|---|
| ALBERTO FERREIRA DE SOUZA | Presidente |
| ANDRE GEORGHTON CARDOSO PACHECO | Examinador Interno |
| JURANDY GOMES DE ALMEIDA JUNIOR | Examinador Externo |
Summary: We examined the effects of integrating data containing divergent information, particularly
concerning anti-vaccination narratives, in training a GPT-2 language model by fine-tuning
it using content from anti-vaccination groups and channels on Telegram. Our objective
was to analyze the model’s ability to generate coherent and rationalized texts compared
to a model pre-trained on OpenAI’s WebText dataset. The results demonstrate that
fine-tuning a GPT-2 model with biased data leads the model to perpetuate these biases in
its responses, albeit with a certain degree of rationalization, highlighting the importance of
using reliable and high-quality data in the training of natural language processing models
and underscoring the implications for information dissemination through these models.
We also explored the impact of data poisoning by incorporating anti-vaccination messages
combined with general group messages in different proportions, aiming to understand how
exposure to biased data can influence text generation and the introduction of harmful
biases. The experiments highlight the change in frequency and intensity of anti-vaccination
content generated by the model and elucidate the broader implications for reliability
and ethics in using language models in sensitive applications. This study provides social
scientists with a tool to explore and understand the complexities and challenges associated
with misinformation in public health through the use of language models, particularly in
the context of vaccine misinformation.
