How can LLMs think better with inner dialogue?
Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning
September 20, 2024
https://arxiv.org/pdf/2409.12618-
Main Topic: The paper introduces "Iteration of Thought" (IoT), a new method for making LLMs more accurate and efficient by mimicking human-like iterative prompting. Instead of a fixed line of reasoning, IoT uses an "Inner Dialogue Agent" (IDA) to adjust the prompting in real-time based on the LLM's responses, leading to more dynamic and context-aware answers.
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Key Points for LLM-Based Multi-Agent Systems:
- Dynamic Prompting: IoT's IDA acts like a guide, creating a more flexible multi-agent system compared to static methods.
- Improved Reasoning: Tests show IoT beats standard methods (Chain-of-Thought) in complex tasks like question answering (GPQA, HotpotQA) and problem-solving (Game of 24, Mini Crosswords), highlighting its potential for multi-agent scenarios.
- Autonomous vs. Guided: The paper explores two IoT versions: one where the LLM decides when to stop iterating (autonomous), and one with a fixed number of iterations (guided), each having trade-offs.
- Ensemble Potential: The IDA could be expanded into a team of specialized sub-agents, further boosting multi-agent capabilities.
- Explainability: The IDA's prompts provide a clear record of the reasoning process, making the LLM's decisions more understandable.
- Future Directions: Combining IoT with other techniques like Self-Consistent CoT and integrating more specialized LLMs are promising areas for enhancing multi-agent system performance.