- if, why, where?
- Classification task
- Rise of LLM
- Rnn
- Attention
- Bert gpt
- Fw: roberta
- Demand of explainability
- Explaining methods (lime, gradient)
- Bonr in text classification
- fw
- Depression, dataset
- born
Today i’m presentin you my thesis work.
Classification task, we can deal in different manners Machine learning automated tools Neural networks are nowdays the Large Language models (gpt, bert) This is a cat, we’re asking ourself why this is a cat Since the introduction more and more complex models, we are worried about explainability Can we extract patterns? Training bias dangerous? Bisect our enemy, r Fight against our superhero *(role perspective is purely random intended only for narrative purpose, no bias on roleplay (maybe)) Fo Task of of depression explainability is a must Depression not easy, mainly oral or writing ML require a lot of data
why large? too big to train > transfer learnin is the key point > lstm sequentially, embeddings parallel
simplification based lime gradient based, saliency maps
dataset liwc born cls token
text to lexical distribution lexical_weights of a perceptron w ridge regression of lexical_weights_w <> embeddings = P coeff matrix P_coeff <>
saliency cls attention