Abstract: Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity: Proposes an innovative adaptive QA framework that can determine the optimal response strategy for LLMs based on the complexity of the user’s query. This system introduces flexibility in handling a range of query complexities, adjusting between iterative and single-step retrieval-augmented LLMs, and no-retrieval methods.

• Employs a classifier to predict complexity levels of incoming queries.
• Enhances QA systems’ efficiency and accuracy across varying levels of query complexity.
• Validation on open-domain QA datasets demonstrates improved performance over baselines.
• Adaptive strategy code is accessible on GitHub.

This paper’s importance lies in its potential to streamline LLM interactions by tailoring responses to query complexity, thereby enhancing the user experience. It serves as an example of how AI systems can become more attuned to user requirements, leading to smarter and more efficient AI-assisted information retrieval.