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We investigate the weights of large language models (LLMs) in domain adaptation tasks. We aim to find out how LLMs encode domain information, whether it is shared across domains, and how to take advantage of this knowledge for unsupervised domain adaptation.

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Name		Name	Last commit message	Last commit date
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Poster.pdf		Poster.pdf
README.md		README.md
bert_dataset.py		bert_dataset.py
bert_model.py		bert_model.py
preprocessing.py		preprocessing.py
runner.py		runner.py
train.py		train.py

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Exploring Self-awareness of Domain in Language Models

Please look at the poster we presented regarding this work: Poster

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We investigate the weights of large language models (LLMs) in domain adaptation tasks. We aim to find out how LLMs encode domain information, whether it is shared across domains, and how to take advantage of this knowledge for unsupervised domain adaptation.

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