Boost LLM Accuracy with Retrieval Augmented Generation (RAG) and Reranking

Unlock the Power of Enhanced LLMs: Retrieval-Augmented Generation (RAG) and Reranking

Large Language Models (LLMs) have revolutionized AI, but limitations like hallucinations and outdated information hinder their accuracy. Retrieval-Augmented Generation (RAG) and reranking offer solutions by integrating LLMs with dynamic information retrieval. Let's explore this powerful combination.

Why RAG Enhances LLMs?

LLMs excel at various NLP tasks, as illustrated below:

A taxonomy of solvable language tasks by LLMs | Iván Palomares

However, LLMs sometimes struggle with contextually appropriate responses, generating incorrect or nonsensical information (hallucinations). Furthermore, their knowledge is limited by their training data's "knowledge cut-off" point. For example, an LLM trained before January 2024 wouldn't know about a new flu strain emerging that month. Retraining LLMs frequently is computationally expensive. RAG provides a more efficient alternative.

RAG leverages an external knowledge base to supplement the LLM's internal knowledge. This improves response quality, relevance, and accuracy without constant retraining. The RAG workflow is:

1. Query: The user's question is received.
2. Retrieve: The system accesses a knowledge base, identifying relevant documents.
3. Generate: The LLM combines the query and retrieved documents to formulate a response.

Reranking: Optimizing Retrieval

Reranking refines the retrieved documents to prioritize the most relevant information for the specific query and context. The process involves:

1. Initial Retrieval: A system (e.g., using TF-IDF or vector space models) retrieves a set of documents.
2. Reranking: A more sophisticated mechanism reorders these documents based on additional criteria (user preferences, context, advanced algorithms).

Reranking process | Iván Palomares

Unlike recommender systems, reranking focuses on real-time query responses, not proactive suggestions.

Reranking's Value in RAG-Enhanced LLMs

Reranking significantly enhances RAG-powered LLMs. After initial document retrieval, reranking ensures the LLM uses the most pertinent and high-quality information, boosting response accuracy and relevance, especially in specialized fields.

Reranker Types

Various reranking approaches exist, including:

• Multi-vector rerankers: Use multiple vector representations for improved similarity matching.
• Learning to Rank (LTR): Employs machine learning to learn optimal rankings.
• BERT-based rerankers: Leverage BERT's language understanding capabilities.
• Reinforcement learning rerankers: Optimize rankings based on user interaction data.
• Hybrid rerankers: Combine multiple strategies.

Building a RAG Pipeline with Reranking (Langchain Example)

This section demonstrates a simplified RAG pipeline with reranking using the Langchain library. (Complete code available in a Google Colab notebook – link omitted for brevity). The example processes text files, creates embeddings, uses OpenAI's LLM, and incorporates a custom reranking function based on cosine similarity. The code showcases both a version without reranking and a refined version with reranking enabled.

Further Exploration

RAG is a crucial advancement in LLM technology. This article covered reranking's role in enhancing RAG pipelines. For deeper dives, explore resources on RAG, its performance improvements, and Langchain's capabilities for LLM application development. (Links omitted for brevity).

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