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RAG (Retrieved-Augmented Generation) for LLM:

A Curated Collection

This repository is dedicated to curating high-quality papers, resources, and tools related to RAG in the context of Large Language Models (LLM). RAG bridges the gap between retrieval-based and generation-based methods, offering a promising approach for knowledge-intensive tasks.

Table of Content

Benchmarks

Name Links
1) RAGAS: Automated Evaluation of Retrieval Augmented Generation Paper
2) Benchmarking Large Language Models in Retrieval-Augmented Generation Paper

Tutorials

Name Links
1) ACL 2023 Tutorial: Retrieval-based Language Models and Applications Web, Github

RAG-papers

LLM-based

Paper Links
1) RETRO : Improving language models by retrieving from trillions of tokens Paper
2) Atlas : Few-shot Learning with Retrieval Augmented Language Models Paper, Github
3) RALM : In-Context Retrieval-Augmented Language Models Paper, Github
4) Self-RAG : LLM-based Retrieval by generating and reflecting on retrieved passages Paper, Github
5) Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy Paper
6) Exploring the Integration Strategies of Retriever and Large Language Models Paper
7) Generator-Retriever-Generator: A Novel Approach to Open-domain Question Answering Paper, Github
8) REPLUG : Retrieval-Augmented Black-Box Language Models Paper
9) Surface-Based Retrieval Reduces Perplexity of Retrieval-Augmented Language Models Paper, Github
10) Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System Paper, Github
11) Beam Retrieval: General End-to-End Retrieval for Multi-Hop Question Answering Paper, Github
12) Retrieval-Generation Synergy Augmented Large Language Models Paper
13) Enabling Large Language Models to Generate Text with Citations Paper, Github
14) Improving Language Models by Retrieving From Trillions of Tokens Paper
15) Internet-Augmented Language Models through Few-Shot Prompting for Open-Domain Question Answering Paper
16) Rethinking with Retrieval: Faithful Large Language Model Inference Paper
17) Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions Paper
18) Active Retrieval Augmented Generation Paper
19) Retrieve Anything To Augment Large Language Models Paper
20) ReAct: Synergizing Reasoning and Acting in Language Models Paper
21) Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback Paper
22) Teaching Language Models to Support Answers with Verified Quotes Paper
23) Augmented Language Models: a Survey Paper
24) LeanDojo: Theorem Proving with Retrieval-Augmented Language Models Paper
25) Retrieval-Augmented Multimodal Language Modeling Paper
26) RA-DIT: RETRIEVAL-AUGMENTED DUAL INSTRUCTION TUNING Paper
27) Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT Paper
28) Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-In Paper, Github
29) Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering Paper, Github
30) Self-Knowledge Guided Retrieval Augmentation for Large Language Models Paper

Language Agents

Paper Links
1) Cognitive Architectures for Language Agents Paper, Github
2) Generative Agents: Interactive Simulacra of Human Behavior

(grounding, reasoning, retrieval, learning)

Paper, Github
3) CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

(grounding, reasoning, retrieval)

Paper, Github
4) Voyager: An Open-Ended Embodied Agent with Large Language Models

(grounding, reasoning, retrieval, learning)

Paper, Github
5) ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

(grounding, reasoning, retrieval)

Paper, Github
6) ExpeL: LLM Agents Are Experiential Learners

(grounding, reasoning, retrieval, learning)

Paper
7) Synergistic Integration of Large Language Models and Cognitive Architectures for Robust AI: An Exploratory Analysis

(grounding, reasoning, retrieval, learning)

Paper

Related References

  • CoALA: Awesome Language Agents: Github

Acknowledgement

We welcome contributions! If you come across a relevant paper or resource that should be included, please open a pull request or issue. Ensure that your suggestions adhere to the repository's standards.

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