Adaptive-RAG

Pattern Name: Adaptive Retrieval-Augmented Question Answering (Adaptive-RAG)

Context/Background:

• Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by providing access to external knowledge bases, improving response accuracy in question-answering tasks.
• However, traditional RAG methods often use a one-size-fits-all approach, regardless of the complexity of the question, leading to inefficiencies.

Problem: RAG models need a way to dynamically adapt their information retrieval and answer generation processes in order to optimize performance across queries of varying complexity.

Forces:

• Efficiency: The desire to reduce computational overhead, especially for simpler queries.
• Accuracy: The need to maintain high accuracy across simple and complex questions by providing the appropriate retrieval and reasoning depth.
• Adaptability: The ability to handle a wide range of question complexities without manual intervention or predetermined rules.

Solution:

1. Complexity Classifier:

• A classifier is trained on a dataset of questions with labeled complexity levels (e.g., simple, single-step, multi-step).
• This classifier predicts the complexity of new, incoming queries.

2. Adaptive Retrieval & Reasoning:

• Simple Questions: If classified as simple, a streamlined or potentially no-retrieval strategy would be applied.
• Single-Step Questions: Queries might trigger a standard retrieval-augmented approach.
• Multi-Step Questions: Queries would initiate a more in-depth retrieval and reasoning process, potentially involving iterative retrieval steps.

Detailed Solution Steps

1. Complexity Classifier Training

• Data Collection:
• Strategy 1: Outcome-Based Labeling

• Run a diverse set of queries through different retrieval-augmented LLMs (non-retrieval, single-step, multi-step).
• Label queries based on the simplest successful model:

• Successful with non-retrieval -> “Simple”
• Successful with single-step, but not non-retrieval -> “Single-step”
• Requires multi-step for success -> “Multi-step”
• Strategy 2: Dataset Bias Labeling
• For queries without labels from Strategy 1:

• Queries from datasets designed for single-hop QA -> “Single-step”
• Queries from datasets designed for multi-hop QA -> “Multi-step”

• Classifier Model: Choose a smaller language model (LM) architecture for efficiency.
• Training: Train the classifier LM on the collected dataset using cross-entropy loss to predict query complexity labels.

2. Adaptive RAG Framework Deployment

• 3. Query Input: Receive a new user query.
• 4. Complexity Prediction:

• Pass the query through the trained complexity classifier.
• The classifier outputs a predicted complexity label (“Simple”, “Single-step”, “Multi-step”).
• 5. Strategy Selection & Execution
• Simple:
• Process the query directly with a non-retrieval LLM.
• Single-step:
• Employ a standard retrieval-augmented LLM:

• Retrieve relevant documents from a knowledge base.
• Augment the query with retrieved information and feed it to the LLM.
• Multi-step:
• Use an iterative retrieval-augmented LLM designed for multi-step reasoning:

• Perform multiple knowledge retrieval rounds.
• Incorporate intermediate results and retrieved information into subsequent steps.
• 6. Answer Generation: The selected LLM generates the final answer based on the chosen strategy and input.

Important Considerations

• The accuracy of the complexity classifier is vital to the success of Adaptive-RAG.
• The specific implementations of non-retrieval, single-step, and multi-step retrieval-augmented LLMs can vary based on the latest research advancements in this field.
• The choice of knowledge base and retrieval models will also impact performance.

Consequences:

• Positive:

• Increased computational efficiency, especially for complex queries.
• Improved accuracy across the board as questions receive tailored approaches.
• A model that conserves resources while providing better responses.
• Potential Challenges:

• Accuracy of the complexity classifier is crucial
• Designing effective strategies for each complexity level

Additional Notes:

• The paper describes experiments demonstrating Adaptive-RAG’s success on open-domain QA datasets and using FLAN-T5 models.
• This pattern represents a shift from one-size-fits-all RAG methods to a more intelligent and context-aware approach.

Paper : https://arxiv.org/abs/2403.14403