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Advancing Question-Answering in Ophthalmology with Retrieval Augmented Generations (RAG): Benchmarking Open-source and Proprietary Large Language Models

Nguyen, Q.; Nguyen, D.-A.; Dang, K.; Liu, S.; Wang, S. Y.; Nguyen, K.; Woof, W. A.; Thomas, P.; Patel, P. J.; Balaskas, K.; Thygesen, J. H.; Wu, H.; Pontikos, N.

2024-11-19 ophthalmology
10.1101/2024.11.18.24317510 medRxiv
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Withdrawal statementThe authors have withdrawn this manuscript due to the inadvertent use of copyrighted material without necessary permissions from the rights holder. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

Published in Translational Vision Science & Technology · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.