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Researchers teach LLMs to solve complex planning challenges
- MIT researchers have developed a framework to guide large language models (LLMs) in solving complex planning problems like a human.
- The framework allows users to describe the problem in natural language without needing specific examples for training the LLM.
- The model encodes the user's text prompt for efficient solving of planning challenges using optimization software.
- During problem formulation, the LLM checks its work at intermediate steps to rectify errors and ensure accurate planning.
- The framework showed an 85 percent success rate in solving challenges like minimizing warehouse robot travel distance.
- It can be applied to tasks such as crew scheduling and factory machine time management.
- The research introduces a smart assistant framework that finds optimal plans even for complex or unusual rules.
- The framework, LLM-Based Formalized Programming (LLMFP), prompts LLMs to reason about problems and determine solutions.
- LLMFP self-assesses the solution and corrects any errors in the problem formulation for an accurate final plan.
- The framework achieved an average success rate between 83-87% in diverse planning problems across multiple LLMs.
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