Retrieval Augmented Generation Research: 2017-2024

This post seeks to give an overview of the research in this field and explain the difference between frozen, advanced, and fully dynamic RAG.

This article draws inspiration from the excellent lecture "Stanford CS25: V3 I Retrieval Augmented Language Models" by Douwe Kiela, who, along with Patrick Lewis, Ethan Perez, et al., invented RAG in May 2020.

The idea of enabling computers to extract information from a knowledge base to assist in language tasks goes back decades, with early question-answering systems from the 1960s and IBM's Watson Jeopardy system having similar conceptual underpinnings. To understand the origins of the first RAG-like systems in 2017 and its invention in 2021 we have to understand the underlying retrieval technology.

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Technology tree of RAG research development featuring representative works By Gao et al 2023

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Taxonomy of RAG's Core Components

Retrieval

Sparse vs Dense Retrieval

ORQA: Latent Retrieval for Weakly Supervised Open Domain Question Answering

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Vector DBs and Sparse & Dense Hybrids

ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

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SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking

With term expansion on our query we will a much larger overlap because we’re now able to identify similar words.|1000

DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

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SANTA: Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data

🧊 Frozen vs Dynamic RAG 🔥

The industry has mostly viewed the components of the RAG architecture as separate components that work in isolation.
We can call this “Frozen RAG”. In contrast, some research has focused on iteratively improving the individual components (we can call this “Advanced RAG”).

Ideally, in a “Fully Dynamic” model, the gradients from the loss function would flow back into the entire system (end-to-end training): retriever, generator, and document encoder.
However, this is computationally challenging and has not been done successfully.

🔥 Dynamic Retriever but Fixed Generator 🧊

In-Context Retrieval-Augmented Language Models

REPLUG: Retrieval-Augmented Black-Box Language Models

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DREditor: A Time-efficient Approach for Building a Domain-specific Dense Retrieval Model

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🧊 Fixed Retriever but Dynamic Generator 🔥

FiD: Fusion-in-Decoder

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KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering

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SURGE:Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation

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KNN-LM: Generalization through Memorization: Nearest Neighbor Language Models

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RAG: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

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RETRO: Improving language models by retrieving from trillions of tokens

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Fully Dynamic RAG

REALM: Retrieval-Augmented Language Model Pre-Training

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Other Retrieval Research

RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

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FLARE: Forward-looking active retrieval augmentation

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HyDE: Hypothetical Document Embeddings

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MuGI: Enhancing Information Retrieval through Multi-Text Generation Integration with Large Language Models

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Query Rewriting for Retrieval-Augmented Large Language Models

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Lost in the Middle: How Language Models Use Long Contexts

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Augmentation/interactivity

CRAG: Corrective Retrieval Augmented Generation

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WebGPT: Browser-assisted question-answering with human feedback

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Toolformer: Language Models Can Teach Themselves to Use Tools

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Gorilla: Large Language Model Connected with Massive APIs

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Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

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GRIT: Generative Representational Instruction Tuning

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Conclusion

Further Reading

Other summaries/literature reviews

I plan to summarize more of these here when I find the time. If you think I’m missing a paper feel free to leave a comment or DM me about it:

Filtering and ranking

Transformer memory

Multi-modality

Knowledge Graphs & Reasoning

Other Reasoning Techniques

Instruction & Memory

References

Thoughts 🤔 by Soumendra Kumar Sahoo is licensed under CC BY 4.0