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Detecting Lies in Text based on DistilBERT model with few shot learning


Hongxuan Yang

04/09/2024

Supervised by Alexia Zoumpoulaki; Moderated by Neetesh Saxena

Lie Detection in text is a very active research field. From new stories to tweets interest in detecting lies is constantly growing. Many different datasets for training and many different models to do so exist. This project will explore how these models perform within specific contexts and their generalisation across datasets.

Students need to have good coding skills in python and an interest in machine learning including deep learning.


Final Report (04/09/2024) [Zip Archive]

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