How To Use RAG To Improve Your LLM’s Reasoning Skills

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Originally published on Towards AI.

Using Your Data To Build Reasoning Chains 🧠⛓️

Retrieval Augmented Generation (RAG) typically finds its place in enhancing document-based question answering (QnA), effectively leveraging extensive databases to provide contextually relevant information for Large Language Models (LLMs) to formulate precise answers. Traditionally, when looking to boost the reasoning capabilities of LLMs, the go-to strategy has been fine-tuning these models with additional data. However, fine-tuning is not only resource-intensive but also presents scalability challenges.

Interestingly, RAG could potentially offer a more efficient pathway to enhance LLMs’ reasoning skills without the hefty costs of fine-tuning. This intriguing premise is explored in depth in Enhancing LLM Intelligence with ARM-RAG: Auxiliary Rationale Memory for Retrieval Augmented Generation by Eric Melz, which proposes a novel use of RAG beyond its conventional application, aiming to refine and expand the problem-solving prowess of LLMs efficiently.

This blog post will take a deep dive into the mechanics of ARM-RAG, specifically focusing on how it utilizes RAG to craft prompts that refine the reasoning skills of LLMs. By walking through an example, we aim to vividly illustrate this process. Further, we’ll discuss the results and examine the limitations of this approach.

Let’s say we want the LLM to answer a maths question like the following:

Ray buys a pack of hamburger meat… Read the full blog for free on Medium.

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Published via Towards AI