-
Notifications
You must be signed in to change notification settings - Fork 28.2k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Evidentiality-guided Generator - Retrieval model #15387
Comments
cc @patil-suraj |
Hi everyone, Can I work on adding Fusion-in-Decoder (FiD) to Transformers? |
Yeah that makes sense - @patil-suraj actually mentioned the same! |
@bhavitvyamalik - definitely! I think @qqaatw and I would be happy to guide you. Do you want to open a PR for it? |
I was just going through the paper and will open a PR for it! Since this will be my first model contribution to Transformers, I wanted to know if there any guidlines that I should follow for the same? |
@patrickvonplaten - absolutely! After Fusion-in-Decoder (FiD) is merged, I can open a follow-up addition for Evidentiality-guided Generator. @bhavitvyamalik - I think having a look at contributing guidelines would be helpful in the first step :-) |
Update: Preparing the original train/val/test data took some time as they were loading the full wikipedia dump (~13GB) in memory which was not possible from my side. I tried forward pass with available pretrained weights after removing functions that were not required and it worked well! I had a doubt related to Integrating the retrieval in
|
Great question @bhavitvyamalik ! We've discussed this a bit with @patil-suraj as well. @patil-suraj - would you be interested in guiding @bhavitvyamalik here a bit regarding the design? |
Happy to help otherwise as well if you're too busy :-) |
🌟 New model addition
Model description
In this paper, we introduce Evidentiality-guided GGenerator, which incorporates evidentiality of passages---whether a passage contains correct evidence to support the output---into training the generator via multi-task learning of answer generation and evidentiality prediction for retrieval-augmented generation. Experimental results show large improvements across three knowledge intensive tasks: open question answering, fact verification and knowledge-enhanced dialogue.
Open source status
Happy to guide anyone who is interested through adding this model! Seems like it gives some nice improvements over RAG!
@qqaatw - maybe interesting for you ;-)
The text was updated successfully, but these errors were encountered: