LLaMA 4 vs. GPT-4o: Which Is Better for RAGs?

This article compares the performance of Meta's LLaMA 4 Scout and OpenAI's GPT-4o within Retrieval-Augmented Generation (RAG) systems. The evaluation utilizes the RAGAS framework, providing metrics for faithfulness, answer relevancy, and context precision/recall. The experiment reveals distinct behavioral patterns between the two models.

Model Overview:

• LLaMA 4 Scout: Meta's efficient LLaMA 4 variant, known for its large context window (10 million tokens) and improved handling of sensitive queries. Open-source weights facilitate research and customization.
• GPT-4o: OpenAI's latest GPT model, excelling in reasoning, coding, and response quality, while maintaining computational efficiency.

RAG System Implementation and Evaluation:

A RAG system was built using LangChain, FAISS, FastEmbed, and PyMuPDF. The process involved:

1. Library Installation: Installing necessary Python packages.
2. API Key Setup: Configuring OpenAI and Groq API keys.
3. Model Initialization: Creating ChatOpenAI (for GPT-4o) and ChatGroq (for LLaMA 4 Scout) instances.
4. Embedding and Text Splitting: Utilizing FastEmbedEmbeddings and RecursiveCharacterTextSplitter.
5. Document Loading and Chunking: Extracting and processing text from PDF documents.
6. FAISS Index Creation: Generating embeddings and building a FAISS index for efficient similarity search.
7. RAG Core Functions: Defining functions for retrieving relevant chunks and generating answers.
8. RAGAS Evaluation: Using RAGAS to assess faithfulness, answer relevancy, and context precision/recall. A set of questions and corresponding reference answers were defined.

Results and Analysis:

The RAGAS evaluation revealed contrasting behaviors:

• LLaMA 4 Scout: Consistently generated answers, even with irrelevant retrieved context. RAGAS scored these answers as highly relevant but not faithful to the (poor) context. This suggests a tendency to prioritize generating a plausible response, potentially relying on its internal knowledge rather than the provided context.

• GPT-4o: Strictly adhered to instructions, refusing to answer when the retrieved context was insufficient. This resulted in low scores across all metrics, reflecting its prioritization of factual accuracy and avoidance of hallucination. The model correctly identified the retrieval failure and avoided generating inaccurate answers.

Conclusion:

The choice between LLaMA 4 Scout and GPT-4o for RAG depends on the application's requirements. LLaMA 4 Scout's approach may be suitable for applications where plausible answers are prioritized, even at the cost of perfect faithfulness to retrieved information. GPT-4o's conservative approach, prioritizing accuracy and avoiding hallucination, is better suited for high-stakes applications requiring reliable and fact-based responses. The RAGAS framework proved invaluable in objectively assessing and comparing the models' performance.

Frequently Asked Questions (briefly summarized):

• What is RAG? Enhances LLMs by incorporating external information.
• What is RAGAS? An evaluation framework for RAG systems.
• Why compare LLMs in RAG? To understand their behavior with retrieved data.
• Which model is better? Depends on application needs (plausibility vs. accuracy).
• How to improve RAG? Improve retrieval or fine-tune prompts.

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