Dual Engines of Thoughts: A Depth-Breadth Integration Framework for Open-Ended Analysis
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Abstract (arXiv)
We propose the Dual Engines of Thoughts (DEoT), an analytical framework for comprehensive open-ended reasoning. While traditional reasoning frameworks primarily focus on finding "the best answer" or "the correct answer" for single-answer problems, DEoT is specifically designed for "open-ended questions," enabling both broader and deeper analytical exploration. The framework centers on three key components: a Base Prompter for refining user queries, a Solver Agent that orchestrates task decomposition, execution, and validation, and a Dual-Engine System consisting of a Breadth Engine (to explore diverse impact factors) and a Depth Engine (to perform deep investigations). This integrated design allows DEoT to balance wide-ranging coverage with in-depth analysis, and it is highly customizable, enabling users to adjust analytical parameters and tool configurations based on specific requirements. Experimental results show that DEoT excels in addressing complex, multi-faceted questions, achieving a total win rate of 77-86% compared to existing reasoning models, thus highlighting its effectiveness in real-world applications.
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19 of 24 references.
References · 19
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
- 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
- 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,407
- 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,190
- 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
- 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 886
- 2024Understanding the planning of LLM agents: A surveycited by 421
- 2023Large Language Model Guided Tree-of-Thoughtcited by 221
- 2023AutoAgents: A Framework for Automatic Agent Generationcited by 216
7 more
- 2024The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Surveycited by 168
- 2024EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instructioncited by 154
- 2024Ask-before-Plan: Proactive Language Agents for Real-World Planningcited by 60
- 2023ControlLLM: Augment Language Models with Tools by Searching on Graphscited by 59
- 2024ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedbackcited by 29
- 2024Towards Completeness-Oriented Tool Retrieval for Large Language Modelscited by 26
- 2024Towards Hierarchical Multi-Agent Workflows for Zero-Shot Prompt Optimizationcited by 10
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