A curated list of retrieval-augmented generation (RAG) in large language models.
Suggest and discuss possible enhancements on the Potential Additions page.
Table of Content:
2024
2023
- Benchmarking Large Language Models in Retrieval-Augmented Generation
PaperarXiv
2022
- A Survey on Retrieval-Augmented Text Generation
PaperarXiv
2024
2023
-
Active Retrieval Augmented Generation
PaperCodeEMNLPArchitecture -
⭐ REPLUG: Retrieval-Augmented Black-Box Language Models
PaperarXivArchitecture -
Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study
PaperCodeEMNLP -
InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining
PaperCodearXiv -
Retrieve Anything To Augment Large Language Models
PaperCodearXiv -
Reimagining Retrieval Augmented Language Models for Answering Queries
PaperACL -
In-Context Retrieval-Augmented Language Models
PaperCodeTACLArchitecture -
Query Rewriting for Retrieval-Augmented Large Language Models
PaperCodeEMNLP -
Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute
PaperPMLR -
Universal Information Extraction with Meta-Pretrained Self-Retrieval
PaperCodeACL -
RAVEN: In-Context Learning with Retrieval Augmented Encoder-Decoder Language Models
PaperarXiv -
Unlimiformer: Long-Range Transformers with Unlimited Length Input
PaperCodeNeurIPSArchitecture -
Nonparametric Masked Language Modeling
PaperCodeACLTraining
2022
-
Improving language models by retrieving from trillions of tokens
PaperBlogPMLRArchitecture -
⭐ Atlas: Few-shot Learning with Retrieval Augmented Language Models
PaperCodeBlogTrainingJMLR -
You can't pick your neighbors, or can you? When and how to rely on retrieval in the kNN-LM
PaperACLArchitecture -
Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval
PaperCodeICMLArchitecture -
Training Language Models with Memory Augmentation
PaperEMNLPTraining -
Unsupervised Dense Information Retrieval with Contrastive Learning
PaperCodearXivTraining -
Teaching language models to support answers with verified quotes
PaperarXivApplication -
kNN-Prompt: Nearest Neighbor Zero-Shot Inference
PaperCodeEMNLPApplication
2021
-
Efficient Nearest Neighbor Language Models
PaperCodeEMNLPArchitecture -
Mention Memory: incorporating textual knowledge into Transformers through entity mention attention
PaperCodearXivArchitecture
2020
-
⭐ REALM: Retrieval-Augmented Language Model Pre-Training
PaperCodeHuggingFacePMLRArchitecture -
Generalization through Memorization: Nearest Neighbor Language Models
PaperCodeICLRArchitecture -
Entities as Experts: Sparse Memory Access with Entity Supervision
PaperEMNLPArchitecture -
Dense Passage Retrieval for Open-Domain Question Answering
PaperCodeEMNLPTraining -
⭐ Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
PaperHuggingFaceNeurIPS
2024
2023
-
Stanford CS25: V3 I Retrieval Augmented Language Models
Douwe Kiela (2023)Lecture
Video -
Building RAG-based LLM Applications for Production
Anyscale (2023)Tutorial
Blog -
Multi-Vector Retriever for RAG on tables, text, and images
LangChain (2023)Tutorial
Blog -
Retrieval-based Language Models and Applications
Asai et al. (2023)TutorialACL
WebsiteVideo -
Advanced RAG Techniques: an Illustrated Overview
Ivan Ilin (2023)Tutorial
Blog -
Retrieval Augmented Language Modeling
Melissa Dell (2023)Lecture
Video
2024
- Towards Knowledgeable Language Models
Zoey Sha Li, Manling Li, Michael JQ Zhang, Eunsol Choi, Mor Geva, Peter Hase
@ACL2024, August 12-17
Website
-
LangChain
LangChain is a framework for developing applications powered by language models.
Website -
LlamaIndex
LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models.
Website -
Verba
Verba is an open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box.
Website -
NEUM
Open-source RAG framework optimized for large-scale and real-time data.
Website -
Unstructured
Unstructured.io offers a powerful toolkit that handles the ingestion and data preprocessing step, allowing you to focus on the more exciting downstream steps in your machine learning pipeline. Unstructured has over a dozen data connectors that easily integrate with various data sources, including AWS S3, Discord, Slack, Wikipedia, and more.
Website
- Awesome LLM RAG
- Awesome RAG
- Awesome LLM with RAG
- RAG-Survey
- Awesome LLM Reader: A Repository of Retrieval-augmented LLMs