Search Results for author:
Found 3 papers, 3 papers with code
QAEncoder: Towards Aligned Representation Learning in Question Answering System
Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses.
SynthVLM: High-Efficiency and High-Quality Synthetic Data for Vision Language Models
Crucially, our method's reliance on purely generated data ensures the preservation of privacy, achieving SoTA performance with just 100k data points (only 18% of the official dataset size).
Retrieval-Augmented Generation for AI-Generated Content: A Survey
We first classify RAG foundations according to how the retriever augments the generator, distilling the fundamental abstractions of the augmentation methodologies for various retrievers and generators.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
