Extending Decoders with an Integrated Encoder
This repo holds the code for training encoders that embed the final hidden state from large decoder models. To our knowledge, CoTrEnD is the first architecture to leverage a contrastive loss to train an encoder from a decoder. It was developed as part of the 24h Meta LLAMA-3 hackathon May 2024 by Abhishek Singh, Arthur Böök, and Wian Stipp.
The motivation behind the CoTrEnD project is to utilize on the rich hidden states that are generated within large decoders. Rather than separating the embedder from the decoder as one typically would in a RAG approach, CoTrEnD integrates the encoder on top of the decoder. This allows the encoder to leverage the semantic information already captured within the decoder's hidden states.
The CoTrEnD architecture is a simple extension of the decoder-only model. The encoder is trained to embed the final hidden state of the decoder. The encoder is trained using a contrastive loss, which encourages the encoder to embed similar hidden states for similar inputs, and dissimilar hidden states for dissimilar inputs.
The CoTrEnD project includes a user interface that allows users to interact with the model. The user interface is built using Streamlit with two modes of operation.
The user can ask anything in the question field, and the CoTrEnD model will do a embedding search over the vectorstore to augment the generated answer.
The user can enter a medical entity in the entity field, and the CoTrEnD model will return the most similar document from the vectorstore.
|
|
|