mlsanityData to 7 Oct 2026

Introducing Super RAGs in Mistral 8x7B-v1

arXiv 2404.08940 ↗First posted 13 Apr 2024cs.IR cs.CL cs.LGcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

The relentless pursuit of enhancing Large Language Models (LLMs) has led to the advent of Super Retrieval-Augmented Generation (Super RAGs), a novel approach designed to elevate the performance of LLMs by integrating external knowledge sources with minimal structural modifications. This paper presents the integration of Super RAGs into the Mistral 8x7B v1, a state-of-the-art LLM, and examines the resultant improvements in accuracy, speed, and user satisfaction. Our methodology uses a fine-tuned instruct model setup and a cache tuning fork system, ensuring efficient and relevant data retrieval. The evaluation, conducted over several epochs, demonstrates significant enhancements across all metrics. The findings suggest that Super RAGs can effectively augment LLMs, paving the way for more sophisticated and reliable AI systems. This research contributes to the field by providing empirical evidence of the benefits of Super RAGs and offering insights into their potential applications.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 11 of its 16 references that are in the map. First posted on arXiv 13 Apr 2024.

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11 of 16 references.

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Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 10

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2024Mixtral of Expertscited by 1,785
  3. 2023A Survey on Evaluation of Large Language Modelscited by 1,227
  4. 2023Instruction Tuning for Large Language Models: A Surveycited by 617
  5. 2023OctoPack: Instruction Tuning Code Large Language Modelscited by 178
  6. 2024The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)cited by 159
  7. 2020Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approachcited by 12
  8. 2023EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form Narrative Text Generationcited by 12
  9. 2024Transformer tricks: Precomputing the first layercited by 3
  10. 2021Cache-Efficient Fork-Processing Patterns on Large Graphscited by 1

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  7. 2025Comparing the Performance of LLMs in RAG-based Question-Answering: A Case Study in Computer Science Literature
  8. 2024ERATTA: Extreme RAG for Table To Answers with Large Language Models