Table of Contents
Retrieval-Augmented Methods (RAG)
Overviews
Papers
During Pre-Training
Related Pages
Retrieval-Augmented Methods (RAG)
Overviews
Gao et al 2023 - Retrieval-Augmented Generation for Large Language Models: A Survey
Papers
Lewis et al 2020 - Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Liu et al 2021 - What Makes Good In-Context Examples for GPT-3?
Cited by
Rubin 2021
Rubin et al 2021 - Learning To Retrieve Prompts for In-Context Learning
Is this the first retrieval-based in-context learning paper?
Poesia et al 2022 - Synchromesh: Reliable code generation from pre-trained language models
One of the first retrieval-based in-context learning papers
Wang et al 2022 - Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data
Jiang et al 2023 - Active Retrieval Augmented Generation
Izacard et al 2022 - Few-shot Learning with Retrieval Augmented Language Models
Learns the retrieval model
Lin et al 2023 - RA-DIT: Retrieval-Augmented Dual Instruction Tuning
Fine-tunes a language model so it's better for retrieval augmented use. Best performance occurs after a small number of fine-tuning steps (<500 steps!).
Zhang et al 2024 - Accelerating Retrieval-Augmented Language Model Serving with Speculation
Zheng et al 2025 - Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievals
During Pre-Training
Guu et al 2020 - REALM: Retrieval-Augmented Language Model Pre-Training
RETRO:
Borgeaud et al 2021 - Improving Language Models by Retrieving from Trillions of Tokens
Wang et al 2023 - Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study
Related Pages
Language Model
Prompting