# Decomposed Prompting: A Modular Approach for Solving Complex Tasks

**Authors:** Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, Ashish Sabharwal  
**arXiv:** [2210.02406](https://arxiv.org/abs/2210.02406)  
**Date:** 2022-10-05  
**Summary page:** https://www.summarizepaper.com/en/paper/2210.02406v2/

## Key points

- Authors introduce Decomposed Prompting as a novel approach for solving complex tasks using Large Language Models (LLMs)
- Decomposed Prompting breaks down complex tasks into simpler sub-tasks through prompting
- Sub-tasks are assigned to a library of specialized LLMs for handling them
- Modular structure allows optimization of prompts for specific sub-tasks and easy replacement with more effective prompts or models
- Outperforms previous methods using few-shot prompting with GPT3
- Enables decomposition of challenging sub-tasks into more manageable ones, especially in symbolic reasoning tasks
- Recursive decomposition can handle tasks with smaller inputs due to task complexity arising from input length
- Separate sub-task prompts enhance teaching process in long-context multi-hop QA tasks
- Incorporation of symbolic information retrieval within the framework improves performance in open-domain multi-hop QA scenarios
- Provides datasets, code, and prompts on GitHub for further exploration and implementation by the research community

## Summary

In their paper "Decomposed Prompting: A Modular Approach for Solving Complex Tasks," Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal introduce a novel approach to tackling complex tasks using Large Language Models (LLMs). The authors highlight the limitations of few-shot prompting in handling tasks with increasing complexity or involving challenging reasoning steps within more intricate tasks. To address this issue, they propose Decomposed Prompting, a method that breaks down complex tasks into simpler sub-tasks through prompting. These sub-tasks are then assigned to a library of prompting-based LLMs specialized in handling them. The modular structure of Decomposed Prompting allows for optimization of each prompt for its specific sub-task. Additionally, prompts can be further decomposed if needed and easily replaced with more effective prompts or trained models. The authors demonstrate that the flexibility and modularity of their approach outperform previous methods utilizing few-shot prompting with GPT3. Moreover, on symbolic reasoning tasks, Decomposed Prompting enables the decomposition of challenging sub-tasks into more manageable ones. When task complexity arises from input length, the authors show how recursive decomposition can be applied to handle tasks with smaller inputs. The effectiveness of Decomposed Prompting is further illustrated through evaluations on textual multi-step reasoning tasks. For long-context multi-hop QA tasks, separate sub-task prompts enhance the teaching process. In open-domain multi-hop QA scenarios, the incorporation of symbolic information retrieval within the decomposition framework leads to improved performance on both types of tasks. Overall  offers a versatile and efficient solution for addressing complex tasks by breaking them down into simpler components that can be effectively managed by specialized LLMs. The authors provide access to datasets, code, and prompts related to their work on GitHub for further exploration and implementation by the research community. This paper was published at ICLR 2023 as a conference paper.