Retrieval-Augmented Generation for Large Language Models: A Survey

This paper surveys Retrieval-Augmented Generation (RAG), a hybrid framework that enhances the performance of large language models (LLMs) by combining retrieval-based methods with generation capabilities. By integrating external knowledge retrieval, RAG addresses key limitations of LLMs, such as hallucinations, memory constraints, and outdated knowledge.

RESEARCH

Shubhradeep

This paper surveys Retrieval-Augmented Generation (RAG), a hybrid framework that enhances the performance of large language models (LLMs) by combining retrieval-based methods with generation capabilities. By integrating external knowledge retrieval, RAG addresses key limitations of LLMs, such as hallucinations, memory constraints, and outdated knowledge.

Introduction to RAG

What is RAG?: RAG combines a retriever module, which fetches relevant external knowledge (e.g., documents or database entries), with a generator module, typically an LLM, that uses the retrieved information to produce outputs.
Purpose: It ensures outputs are grounded in factual and up-to-date knowledge, reducing hallucination and improving task-specific performance.

Core Components of RAG

Retriever:
- Retrieves relevant information from external knowledge bases (e.g., Wikipedia, domain-specific corpora).
- Retrieval methods include traditional (BM25), neural (dense embeddings), and hybrid approaches.
Generator:
- Uses the retrieved information as context to generate accurate, coherent, and grounded responses.
- Pre-trained LLMs like GPT or BERT are commonly used.
Retrieval-Augmentation Loop:
- A feedback mechanism ensures the retriever continually improves based on the generator’s needs.

Benefits of RAG

Enhanced Knowledge Access: Models can reference external data instead of relying solely on their training corpus.
Reduced Hallucination: Outputs are grounded in retrieved, verified information.
Domain Adaptability: Allows customization for specific use cases without retraining the entire LLM.
Efficient Updates: Retrieval modules can incorporate new data dynamically, keeping outputs current.

Applications

Question Answering: Retrieving precise facts to answer queries accurately.
Summarization: Generating summaries supported by retrieved documents.
Customer Support: Dynamic knowledge retrieval for real-time responses.
Legal and Healthcare Domains: Providing domain-specific, factual outputs based on expert sources.

Challenges

Retrieval Quality: Ensuring retrieved documents are relevant and accurate.
Integration: Effective interaction between the retriever and generator modules.
Latency: Retrieving external data can introduce delays in response times.
Evaluation: Measuring the factual accuracy and coherence of RAG systems is complex.

Future Directions

Improved Retrieval Techniques: Leveraging multimodal retrieval and neural retrieval systems.
Scalable Architectures: Reducing latency and computational cost.
Dynamic Knowledge Updates: Real-time integration of new information sources.
Alignment and Safety: Ensuring generated content aligns with factual and ethical standards.
Explainability: Enhancing transparency in how retrieved knowledge influences generated outputs.

Conclusion: RAG is a promising framework for bridging LLM capabilities with dynamic, grounded knowledge access. It addresses many challenges of traditional LLMs, making it particularly useful for real-world applications requiring accuracy, adaptability, and reliability. The survey highlights RAG’s transformative potential while emphasizing areas for further research and optimisation.