Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees
Researchers have developed a Learning-to-Defer framework to improve the efficiency of extractive question answering (EQA) using large language models. This method intelligently allocates queries to specialized models, ensuring high-confidence predictions while minimizing computational costs. Tested on datasets like SQuADv1 and TriviaQA, the framework demonstrated enhanced answer reliability and significant reductions in computational overhead, making it suitable for scalable EQA deployments. AI
IMPACT Optimizes LLM resource allocation for question answering, potentially reducing costs and improving performance in specialized applications.