Right now, it seems that most of the community if fixed on the RAG (excluding Prompt Engineering). However, there is a technique called Iterative RAG (Ma et al., 2023; Li et al., 2024; Chan et al., 2024; Shi et al., 2024).
This is a more advanced approach in natural language processing and generative AI that enhances the interaction between information retrieval and generation by refining outputs through multiple iterations.
1. What is RAG?
RAG integrates two main components:
- Retriever: Finds relevant documents or data from an external knowledge base. This is typically the task of some connector.
- Generator: Generates a response or output based on the retrieved information.This is covered by the model itself.
The aim is to use external knowledge for factually grounded and contextually relevant outputs.The data can be stored in some database, can be retrieved from an external service, read from documents etc.
2. What Makes Iterative RAG Different?
Iterative RAG improves upon standard RAG by performing multiple cycles of refinement. It iteratively improves the quality of output by revisiting retrieval and generation steps.
Iteration Process and Feedback Loop:
- Initial Retrieval: Retrieve a set of documents or data points. (Same as RAG).
- Generation: Produce an output based on the retrieved information.(Same as RAG)
- Feedback Loop: Analyze the output to identify gaps or areas for improvement.
- Refinement Retrieval: Use feedback to refine the search for better data.(Same as RAG)
- Regeneration: Generate a new output based on the refined retrieval.(Same as RAG)
This loop continues until:
- The output meets a predefined quality threshold, or
- A maximum number of iterations is reached.
3. Advantages of Iterative RAG
- Improved Accuracy: Addresses errors or missing information through iterations.
- Contextual Relevance: Refines context to better align the final response with the query.
- Dynamic Adaptation: Adjusts retrieval and generation strategies dynamically.
This process seems to be evolved over time. I guess, that reasoning might be a bit inspired by iterative process of the feedback introduced by the Iterative RAG.
4. Applications
- Question Answering: Produces detailed, factually accurate answers by refining retrieved knowledge.
- Document Summarization: Ensures summaries include all relevant information.
- Conversational AI: Enhances dialogue coherence by refining context and revisiting prior responses.
5. Challenges
- Computational Cost: Iterations increase latency and resource usage.
- Optimization Complexity: Balancing retrieval and generation across iterations can be very tricky task.
- Risk of Overfitting: Excessive iterations might lead to overly specific or biased outputs.
Recap
Iterative RAG is a significant advancement in combining retrieval and generation systems, offering a robust way to handle complex queries and generate high-quality, accurate responses.Although RAG methods achieve strong performance on multi-hop tasks like HotpotQA, there are huge limitations.
For example, RAG is chunk-based and it struggles with knowledge-intensive tasks (Wang et al., 2024a), because chunks contain excessive text noise and do not capture the relation betwen information. With this limitation LLMs cannot effectively use augmented knowledge.