# Enhancing LLM Intelligence with ARM-RAG: Auxiliary Rationale Memory for Retrieval Augmented Generation

arXiv: https://arxiv.org/abs/2311.04177 · HTML: https://mlsanity.com/paper/2311.04177/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 7 Nov 2023.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 10 of 10)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification](https://mlsanity.com/paper/2011.03088/) (2020)
- [Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval](https://mlsanity.com/paper/2101.00436/) (2021)
- [ELECTRA is a Zero-Shot Learner, Too](https://mlsanity.com/paper/2207.08141/) (2022)

## Built on by (most cited first, 14 of 14)

- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security](https://mlsanity.com/paper/2401.05459/) (2024)
- [Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control](https://mlsanity.com/paper/2505.18279/) (2025)
- [Retrieval-Augmented Generation-based Relation Extraction](https://mlsanity.com/paper/2404.13397/) (2024)
- [MetaLadder: Ascending Mathematical Solution Quality via Analogical-Problem Reasoning Transfer](https://mlsanity.com/paper/2503.14891/) (2025)
- [LightHouse: A Survey of AGI Hallucination](https://mlsanity.com/paper/2401.06792/) (2024)
- [RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration](https://mlsanity.com/paper/2503.02800/) (2025)
- [Overcoming LLM Challenges using RAG-Driven Precision in Coffee Leaf Disease Remediation](https://mlsanity.com/paper/2405.01310/) (2024)
- [Enhancing Commentary Strategies for Imperfect Information Card Games: A Study of Large Language Models in Guandan Commentary](https://mlsanity.com/paper/2406.17807/) (2024)
- [LLM-R: A Framework for Domain-Adaptive Maintenance Scheme Generation Combining Hierarchical Agents and RAG](https://mlsanity.com/paper/2411.04476/) (2024)
- [Leveraging Retrieval-Augmented Generation for Persian University Knowledge Retrieval](https://mlsanity.com/paper/2411.06237/) (2024)
- [Coordinated Semantic Alignment and Evidence Constraints for Retrieval-Augmented Generation with Large Language Models](https://mlsanity.com/paper/2603.04647/) (2026)
- [SOMA: Efficient Multi-turn LLM Serving via Small Language Model](https://mlsanity.com/paper/2605.11317/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are smart but forgetful. Recent studies, (e.g., (Bubeck et al., 2023)) on modern LLMs have shown that they are capable of performing amazing tasks typically necessitating human-level intelligence. However, unlike humans, frozen LLMs do not improve over time; they neither acquire new knowledge nor learn from their successes or failures. Some approaches to improving the intelligence of LLMs include fine-tuning models based on problem-solving performance (Zelikman et al., 2022), and building bigger and more sophisticated models (Bubeck et al., 2023). However, these methods have the drawback of requiring substantial data and computational resources to retrain existing models. In this paper, we explore the use of Retrieval Augmented Generation, also known as RAG (Lewis et al., 2021) to improve problem-solving performance. We propose ARM-RAG (Auxiliary Rationale Memory for Retrieval Augmented Generation), a system that learns from its successes without incurring high training costs. We demonstrate that the storage and subsequent retrieval of reasoning chains have a positive influence on performance in grade-school math problems.