Dynamic Strategy Planning for Efficient Question Answering with Large Language Models
Tanmay Parekh, Pradyot Prakash, Alexander Radovic, Akshay Shekher, Denis Savenkov · 2025
Research has shown the effectiveness of reasoning (e.g., Chain-of-Thought), planning (e.g., SelfAsk), and retrieval augmented generation strategies to improve the performance of Large Language Models (LLMs) on various tasks, such as question answering.However, using a single fixed strategy to answer different kinds of questions is suboptimal in performance and inefficient in terms of generated output tokens and performed retrievals.In our work, we propose a novel technique DyPlan, to induce a dynamic strategy selection process in LLMs, to improve performance and reduce computational costs in question-answering.Dy-Plan incorporates an initial decision step to select the most suitable strategy conditioned on the input question and guides the LLM's response generation accordingly.We extend DyPlan to DyPlan-verify, adding an internal verification and correction process to further enrich the generated answer.Experiments on three prominent multi-hop question answering (MHQA) datasets reveal how DyPlan can improve model performance by 7-13% while reducing the computational cost by 11-32% relative to the best baseline model.Code for this work can be found at https://github.com/ facebookresearch/dyplan.