Awesome RAG 📄🔍

A curated list of awesome resources for RAG (Retrieval Augmentation Generation) exploration.

Table of Contents

• Papers
  • Retrieval
  • RAG vs Finetuning
  • RAG With Knowledge Graphs
  • Evaluation
  • Agents/Tools
  • Survey Papers
• Blogposts

Papers

Retrieval

• RAG: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks - An information retrieval augmented generation model that can be used for various knowledge-intensive NLP tasks. (Lewis, Patrick, et al. 2020)

• RA-DIT: Retrieval-Augmented Dual Instruction Tuning (RA-DIT) - Improve the performance of retrieval-augmented generation models by fine-tuning the retrieval and generation components jointly. (Khattar, Dheeraj, et al. 2021)

• CRAG - Corrective Retrieval Augmented Generation Llama Pack - Enhance the robustness of language model generation by evaluating and augmenting the relevance of retrieved documents through a an evaluator and large-scale web searches. (Shi-Qi Yan, Jia-Chen Gu, et al. 2024)

• Dense x Retrieval: What Retrieval Grnaularity Should We Use? - Improve dense retrieval by using a more fine-grained retrieval granularity as known as Propositions. (Tong Chen, et al. 2023)

• In-Context Learning for Extreme Multi-Label Classification - A retrieval-augmented generation model that can be used for extreme multi-label classification. (Karel, et al. 2021)

• Self-Discover: Large Language Models Self-Compose Reasoning Structures - A retrieval-augmented generation model that can be used for self-composing reasoning structures. (Pei, et al. 2021)

• SELF-RAG: LEARNING TO RETRIEVE, GENERATE, AND CRITIQUE THROUGH SELF-REFLECTION - A retrieval-augmented generation model that can be used for self-reflection. (Akari, et al. 2023)

• Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding - A retrieval-augmented generation model that can be used for table understanding. (Zilong, et al. 2024)

• InstructRetro: Instruction Tuning Post Retrieval-Augmented Pretraining - A Large Language Model pretrained with retrieval before instruction tuning (Wei Ping, et al. 2023)

• RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval - An approach to enhance RAG by creating a summary tree from text chunks, providing deeper insights and overcoming the limitations of short, contiguous text retrieval. (Sarthi, Parth, et al. 2024)

• HiQA: A Hierarchical Contextual Augmentation RAG for Massive Documents QA - An advanced multi-document question-answering framework that integrates cascading metadata and a multi-route retrieval mechanism, enhancing the accuracy of RAG pipeline. (Chen, Xinyue, et al. 2024)

• ActiveRAG: Revealing the Treasures of Knowledge via Active Learning - Enhances RAG by active learning to deepen LLMs' understanding of external knowledge through innovative Knowledge Construction and Cognitive Nexus mechanisms. (Xu, Zhipeng, et al. 2024)

RAG vs Finetuning

• RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture - RAG vs Fine-tuning case study on agriculture domain datasets. (Gupta, Aman, et al. 2024)

RAG With Knowledge Graphs

Evaluation

Agents/Tools

Survey Papers

Contributing

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