How can LLMs explore open-ended questions deeply and broadly?
DUAL ENGINES OF THOUGHTS: A DEPTH-BREADTH INTEGRATION FRAMEWORK FOR OPEN-ENDED ANALYSIS
April 11, 2025
https://arxiv.org/pdf/2504.07872The paper introduces DEoT (Dual Engines of Thoughts), a framework for answering complex, open-ended questions more effectively than current models by combining breadth-first and depth-first analysis.
Key points:
- Dual-Engine System: DEoT uses a breadth-first engine to explore diverse impact factors broadly, followed by a depth-first engine to perform targeted deep dives, mimicking human brainstorming. An "engine controller" manages which engine is active.
- Multi-agent Architecture: DEoT leverages specialized agents (news searcher, event extractor, history analyzer, etc.) within a modular framework. These agents function similarly to tools used in LLM-based multi-agent systems.
- Relevance to LLMs: DEoT uses LLMs (specifically GPT-4 and PerplexityAI's Llama) for various tasks, showcasing potential integration with LLM-based multi-agent systems. The paper emphasizes the limitations of existing LLMs in handling complex open-ended reasoning.
- Focus on Open-Ended Questions: Addresses the gap in LLM benchmarks focused on single-answer tasks, proposing a dataset (N2Q) based on open-ended follow-up questions to news articles. This is relevant to multi-agent systems that need to tackle complex, multifaceted problems.
- Evaluation using LLMs: Employs GPT-4 as an evaluation agent, assessing performance based on multiple criteria (analytical depth, specific arguments, innovation, practicality, logical coherence), which is a valuable approach for evaluating multi-agent system outputs.