A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models

TL;DR

This paper introduces a human-like multi-phases planning framework for large language models, significantly enhancing their ability to perform complex travel planning tasks involving multiple interconnected stages.

Contribution

The study develops a novel human-like reasoning framework with Strategy and Knowledge Blocks, improving LLMs' multi-phases planning capabilities for complex tasks like travel planning.

Findings

Framework achieves 10x performance gain with GPT-4-Turbo.

Enhanced planning efficiency and effectiveness in travel tasks.

Significant improvement over baseline models.

Abstract

Recent studies have highlighted their proficiency in some simple tasks like writing and coding through various reasoning strategies. However, LLM agents still struggle with tasks that require comprehensive planning, a process that challenges current models and remains a critical research issue. In this study, we concentrate on travel planning, a Multi-Phases planning problem, that involves multiple interconnected stages, such as outlining, information gathering, and planning, often characterized by the need to manage various constraints and uncertainties. Existing reasoning approaches have struggled to effectively address this complex task. Our research aims to address this challenge by developing a human-like planning framework for LLM agents, i.e., guiding the LLM agent to simulate various steps that humans take when solving Multi-Phases problems. Specifically, we implement several strategies to enable LLM agents to generate a coherent outline for each travel query, mirroring human planning patterns. Additionally, we integrate Strategy Block and Knowledge Block into our framework: Strategy Block facilitates information collection, while Knowledge Block provides essential information for detailed planning. Through our extensive experiments, we demonstrate that our framework significantly improves the planning capabilities of LLM agents, enabling them to tackle the travel planning task with improved efficiency and effectiveness. Our experimental results showcase the exceptional performance of the proposed framework; when combined with GPT-4-Turbo, it attains $10\times$ the performance gains in comparison to the baseline framework deployed on GPT-4-Turbo.