Results of the summarizing process for the arXiv paper: 2210.02406v2

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.

• 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

Introduction

In recent years, Large Language Models (LLMs) have shown remarkable capabilities in natural language processing tasks. These models are trained on large amounts of data and can generate coherent text, answer questions, and perform other language-based tasks with impressive accuracy. However, as the complexity of these tasks increases, LLMs struggle to maintain their performance levels. This is where few-shot prompting comes into play.

Few-shot prompting is a technique that involves providing a small amount of task-specific information or "prompt" to an LLM to guide its response generation. While this approach has shown promising results in handling complex tasks, it still faces limitations when dealing with intricate reasoning steps within more challenging tasks.

To address this issue, Tushar Khot and his team from the Allen Institute for Artificial Intelligence (AI2) propose Decomposed Prompting - a modular approach for solving complex tasks using LLMs. In their paper titled "Decomposed Prompting: A Modular Approach for Solving Complex Tasks," published at ICLR 2023 conference, they introduce this novel method and demonstrate its effectiveness through various evaluations.

Limitations of Few-Shot Prompting

The authors first highlight the limitations of few-shot prompting in handling complex tasks. They explain how these techniques rely on manually crafted prompts that may not be optimal for every sub-task within a larger task. Moreover, as the complexity of the task increases or when dealing with longer inputs, few-shot prompting struggles to maintain its performance levels.

Introducing Decomposed Prompting

To overcome these limitations, Khot et al. propose Decomposed Prompting - a modular approach that breaks down complex tasks into simpler sub-tasks through prompting. These sub-tasks are then assigned to specialized LLMs trained specifically for them.

The authors emphasize the modularity and flexibility of their approach by explaining how each prompt can be optimized for its specific sub-task without affecting others' performance. Additionally, prompts can be easily replaced with more effective ones or trained models if needed.

Recursive Decomposition for Handling Longer Inputs

When task complexity arises from input length, the authors demonstrate how recursive decomposition can be applied to handle tasks with smaller inputs. This involves breaking down the original task into sub-tasks that are then further decomposed until they reach a manageable size for LLMs to handle effectively.

Effectiveness of Decomposed Prompting on Multi-Step Reasoning Tasks

To showcase the effectiveness of their approach, Khot et al. evaluate Decomposed Prompting on textual multi-step reasoning tasks. They demonstrate how this method enables the decomposition of challenging sub-tasks into simpler ones that can be handled by specialized LLMs. This leads to improved performance compared to previous few-shot prompting methods.

Enhancing Teaching Process in Long-Context Multi-Hop QA Tasks

In long-context multi-hop QA tasks, separate sub-task prompts enhance the teaching process and improve overall performance. The authors also show how incorporating symbolic information retrieval within the decomposition framework leads to better results on both open-domain and symbolic reasoning tasks.

Availability of Datasets, Code, and Prompts

Khot et al. make their work accessible to the research community by providing access to datasets, code, and prompts related to their paper on GitHub. This allows for further exploration and implementation of Decomposed Prompting by other researchers.

Conclusion

In conclusion, "Decomposed Prompting: A Modular Approach for Solving Complex Tasks" offers a versatile and efficient solution for addressing complex tasks using LLMs. By breaking down these tasks into simpler components that can be effectively managed by specialized models through prompting, this approach outperforms previous methods utilizing few-shot prompting with GPT3.

The authors' evaluations demonstrate how Decomposed Prompting is particularly effective in handling longer inputs and intricate reasoning steps within complex tasks. Its modularity allows for optimization of each prompt without affecting others' performance, making it a flexible and adaptable approach.

Through their paper, Khot et al. provide valuable insights into the limitations of few-shot prompting and how Decomposed Prompting can overcome them. This work opens up new possibilities for utilizing LLMs in handling complex tasks and provides a strong foundation for future research in this area.