Problem analysis
Traditional methods for processing long documents suffer from the problems of information loss and focus dispersion, which are mainly limited by the size of the context window.
PRAG Improvement Program
- Parametric compression techniques: Distill document key information into 768-dimensional parameter vectors
- Dynamic integration mechanisms: Automatic weighted merging of TOP-K related document parameters during inference
- self-enhancement model: Parameterized datasets that support multiplexed preprocessing (
data_aug.tar.gz)
Specific operation process
- Prepare your environment: install PyTorch 2.1+ and the transformers library.
- modifications
root_dir_path.pyConfiguring the data storage path - Select the execution mode:
- Fast mode: direct loading of pre-enhanced data
- Customized mode: processing raw datasets such as Wikipedia on your own
best practice
It is recommended that fine-tuned training on specialized field documents can be paired withlangchainThe framework implements multiple rounds of Q&A optimization.
This answer comes from the articlePRAG: Parameterized Retrieval Augmentation Generation Tool for Improving the Performance of Q&A SystemsThe
May not be reproduced without permission:AI productivity tools " How to optimize the comprehensive reasoning of a Q&A system when dealing with long documents?
