Results of the summarizing process for the arXiv paper: 2311.00694v1

Large language models (LLMs) have shown great potential in various domains but still struggle with complex reasoning tasks such as mathematical proofs and advanced mathematics problems. Current approaches to address this challenge involve sampling or searching detailed and low-level reasoning chains but have limitations in exploring the vast solution space, making it difficult for correct solutions to stand out. To overcome these limitations, we propose a novel approach that leverages the creative potential of LLMs by framing them as hierarchical policies through in-context learning. Our approach consists of a visionary leader and a follower. The leader generates multiple diverse high-level problem-solving tactics as hints while the follower executes detailed problem-solving processes based on each hint provided by the leader. The follower explores multiple reasoning chains guided by the leader's directives and generates a solution group for each proposed tactic. In addition to this hierarchical policy framework, we introduce an effective tournament-based approach to select the best solution group among those explored by the follower. This approach enhances problem-solving strategy exploration and improves accuracy on challenging problems in the MATH dataset. Our work contributes meaningful and inspiring hints for problem solving, expands exploration capabilities of LLMs, and ultimately improves the accuracy of final answers. The code for our approach will be released at https://github.com/lz1oceani/LLM-As-Hierarchical-Policy. Overall, our proposed method unleashes LLMs' creative potential by enabling them to explore multiple diverse problem-solving strategies through a hierarchical policy framework. This advancement has significant implications for improving reasoning abilities in challenging tasks across various domains.

• Large language models (LLMs) struggle with complex reasoning tasks in mathematics
• Current approaches involve sampling or searching detailed reasoning chains but have limitations
• Proposed approach leverages LLMs as hierarchical policies through in-context learning
• Approach consists of a visionary leader and a follower for problem-solving tactics
• Follower explores multiple reasoning chains guided by leader's hints
• Tournament-based approach used to select the best solution group
• Enhances problem-solving strategy exploration and improves accuracy on challenging problems
• Code for the approach will be released at a specific GitHub repository
• Unleashes LLMs' creative potential and improves reasoning abilities in challenging tasks across domains

Large language models (LLMs) are computer programs that struggle with complex reasoning tasks in mathematics. Current approaches to solving these tasks involve sampling or searching through detailed reasoning chains, but they have limitations. A proposed approach suggests using LLMs as hierarchical policies through in-context learning. This approach involves a visionary leader and a follower who work together to solve problems using different tactics. The follower explores multiple reasoning chains guided by hints from the leader. A tournament-based approach is used to select the best group of solutions. This approach enhances problem-solving strategy exploration and improves accuracy on difficult problems. The code for this approach will be made available on a specific GitHub repository. It unleashes the creative potential of LLMs and improves their ability to reason in challenging tasks across different areas." Definitions- Large language models (LLMs): Computer programs that struggle with complex reasoning tasks in mathematics. - Hierarchical policies: Using different levels or layers of decision-making strategies. - In-context learning: Learning while considering the current situation or context. - Visionary leader: A person who has great ideas and can guide others towards solving problems. - Follower: Someone who follows the guidance of a leader. - Reasoning chains: Logical steps or thought processes used to solve problems. - Tournament-based approach: A method where different solutions compete against each other, and the best one is selected. - Problem-solving strategy exploration: Trying out different ways of solving problems to find the most effective strategies. - GitHub repository: An online platform

Exploring the Creative Potential of Large Language Models Through Hierarchical Policies

Large language models (LLMs) have shown great potential in various domains, but they still struggle with complex reasoning tasks such as mathematical proofs and advanced mathematics problems. Current approaches to address this challenge involve sampling or searching detailed and low-level reasoning chains, but these methods have limitations when it comes to exploring the vast solution space, making it difficult for correct solutions to stand out. To overcome these limitations, researchers from the Institute of Computing Technology at Chinese Academy of Sciences recently proposed a novel approach that leverages the creative potential of LLMs by framing them as hierarchical policies through in-context learning.

The Hierarchical Policy Framework

The proposed approach consists of two components: a visionary leader and a follower. The leader generates multiple diverse high-level problem-solving tactics as hints while the follower executes detailed problem-solving processes based on each hint provided by the leader. This allows for exploration of multiple reasoning chains guided by the leader's directives and generation of a solution group for each proposed tactic.

In addition to this hierarchical policy framework, researchers introduced an effective tournament-based approach to select the best solution group among those explored by the follower. This approach enhances problem-solving strategy exploration and improves accuracy on challenging problems in the MATH dataset.

Implications for Problem Solving Across Domains

This advancement has significant implications for improving reasoning abilities in challenging tasks across various domains. By leveraging LLMs' creative potential through a hierarchical policy framework, our proposed method enables them to explore multiple diverse problem solving strategies while enhancing their exploration capabilities and ultimately improving accuracy of final answers. The code for our approach is available online at https://github.com/lz1oceani/LLM-As-Hierarchical-Policy .