AI3 Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting https://arxiv.org/abs/2407.08223 Speculative RAG: Enhancing Retrieval Augmented Generation through DraftingRetrieval augmented generation (RAG) combines the generative abilities of large language models (LLMs) with external knowledge sources to provide more accurate and up-to-date responses. Recent RAG advancements focus on improving retrieval outcomes througharxiv.org0. AbstractRAG는 LLM의 생성 기능과.. 2025. 1. 22. VoxelMorph: A Learning Framework for Deformable Medical Image Registration VoxelMorph: A Learning Framework for Deformable Medical Image RegistrationWe present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich defoarxiv.org0. AbstractVoxelMorph은 변형 가능한 pair별 의료 image registration을 .. 2024. 11. 7. (RAG) Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks Retrieval-Augmented Generation for Knowledge-Intensive NLP TasksLarge pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limarxiv.org0. AbstractPretrained LLM은 사실의 지식을 매개변수에 저장하고, downstream NLP 작업에서 미.. 2024. 11. 3. 이전 1 다음
