Enhancing Retrieval-Augmented Generation: A Study of Best Practices
This paper investigates various components and configurations within Retrieval-Augmented Generation (RAG) systems to optimize their performance. It introduces novel RAG designs, including query expansion, new retrieval strategies, and Contrastive In-Context Learning RAG. The study provides actionable insights for developing adaptable and high-performing RAG frameworks by analyzing factors like LLM size, prompt design, and knowledge base characteristics. ✨
Article Points:
1
Contrastive In-Context Learning RAG significantly outperforms other RAG variants.
2
Focus Mode RAG is highly effective, emphasizing concise, high-precision retrieved documents.
3
Knowledge base quality and relevance are more critical than its sheer size for RAG performance.
4
Prompt formulation remains crucial for optimizing RAG system performance.
5
Larger LLM size generally boosts RAG performance, especially on general knowledge tasks.
6
Query Expansion, multilingual KBs, document size, and retrieval stride showed minimal gains.
Enhancing Retrieval-Augmented Generation: A Study of Best Practices
Novel Contributions
Query Expansion
Contrastive In-Context Learning RAG
Multilingual Knowledge Bases
Focus Mode
Key Factors Investigated
LLM Size
Prompt Design
Document Chunk Size
Knowledge Base Size
Retrieval Stride
RAG Architecture
Query Expansion Module
Retrieval Module
Text Generation Module
Evaluation
Datasets
- TruthfulQA
- MMLU
Metrics
- ROUGE
- Embedding Cosine Similarity (ECS)
- MAUVE
- FActScore
Key Findings
Contrastive ICL excels
Focus Mode effective
KB quality over size
Prompt design crucial
Other factors less impactful
Limitations