@inproceedings{parekh-etal-2025-dynamic,
title = "Dynamic Strategy Planning for Efficient Question Answering with Large Language Models",
author = "Parekh, Tanmay and
Prakash, Pradyot and
Radovic, Alexander and
Shekher, Akshay and
Savenkov, Denis",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.336/",
doi = "10.18653/v1/2025.findings-naacl.336",
pages = "6038--6059",
ISBN = "979-8-89176-195-7",
abstract = "Research has shown an effectiveness of reasoning (e.g. Chain-of-Thought), planning (e.g. SelfAsk) and retrieval augmented generation strategies to improve performance of Large Language Models (LLMs) on various tasks, such as question answering. However, using a single fixed strategy for answering all different kinds of questions is sub-optimal in performance and inefficient in terms of generated tokens and retrievals. In our work, we propose a novel technique, DyPlan, to induce a dynamic strategy selection process in LLMs for cost-effective question-answering. DyPlan 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. Experimentation on three prominent multi-hop question answering (MHQA) datasets reveals how DyPlan can improve model performance by 7-13{\%} while reducing the cost by 11-32{\%} relative to the best baseline model."
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<abstract>Research has shown an effectiveness of reasoning (e.g. Chain-of-Thought), planning (e.g. SelfAsk) and retrieval augmented generation strategies to improve performance of Large Language Models (LLMs) on various tasks, such as question answering. However, using a single fixed strategy for answering all different kinds of questions is sub-optimal in performance and inefficient in terms of generated tokens and retrievals. In our work, we propose a novel technique, DyPlan, to induce a dynamic strategy selection process in LLMs for cost-effective question-answering. DyPlan 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. Experimentation on three prominent multi-hop question answering (MHQA) datasets reveals how DyPlan can improve model performance by 7-13% while reducing the cost by 11-32% relative to the best baseline model.</abstract>
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%0 Conference Proceedings
%T Dynamic Strategy Planning for Efficient Question Answering with Large Language Models
%A Parekh, Tanmay
%A Prakash, Pradyot
%A Radovic, Alexander
%A Shekher, Akshay
%A Savenkov, Denis
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F parekh-etal-2025-dynamic
%X Research has shown an effectiveness of reasoning (e.g. Chain-of-Thought), planning (e.g. SelfAsk) and retrieval augmented generation strategies to improve performance of Large Language Models (LLMs) on various tasks, such as question answering. However, using a single fixed strategy for answering all different kinds of questions is sub-optimal in performance and inefficient in terms of generated tokens and retrievals. In our work, we propose a novel technique, DyPlan, to induce a dynamic strategy selection process in LLMs for cost-effective question-answering. DyPlan 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. Experimentation on three prominent multi-hop question answering (MHQA) datasets reveals how DyPlan can improve model performance by 7-13% while reducing the cost by 11-32% relative to the best baseline model.
%R 10.18653/v1/2025.findings-naacl.336
%U https://aclanthology.org/2025.findings-naacl.336/
%U https://doi.org/10.18653/v1/2025.findings-naacl.336
%P 6038-6059
Markdown (Informal)
[Dynamic Strategy Planning for Efficient Question Answering with Large Language Models](https://aclanthology.org/2025.findings-naacl.336/) (Parekh et al., Findings 2025)
ACL