Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning

Original Paper: https://arxiv.org/abs/2308.09658

By: Pengbo Hu, Ji Qi, Xingyu Li, Hong Li, Xinqi Wang, Bing Quan, Ruiyu Wang, Yi Zhou

Abstract:

There emerges a promising trend of using large language models (LLMs) to generate code-like plans for complex inference tasks such as visual reasoning. This paradigm, known as LLM-based planning, provides flexibility in problem solving and endows better interpretability. However, current research is mostly limited to basic scenarios of simple questions that can be straightforward answered in a few inference steps. Planning for the more challenging multi-hop visual reasoning tasks remains under-explored. Specifically, under multi-hop reasoning situations, the trade-off between accuracy and the complexity of plan-searching becomes prominent. The prevailing algorithms either address the efficiency issue by employing the fast one-stop generation or adopt a complex iterative generation method to improve accuracy. Both fail to balance the need for efficiency and performance. Drawing inspiration from the dual system of cognition in the human brain, the fast and the slow think processes, we propose a hierarchical plan-searching algorithm that integrates the one-stop reasoning (fast) and the Tree-of-thought (slow). Our approach succeeds in performance while significantly saving inference steps. Moreover, we repurpose the PTR and the CLEVER datasets, developing a systematic framework for evaluating the performance and efficiency of LLMs-based plan-search algorithms under reasoning tasks at different levels of difficulty. Extensive experiments demonstrate the superiority of our proposed algorithm in terms of performance and efficiency. The dataset and code will be release soon.

Summary Notes

Blog Post: Simplifying Multi-hop Visual Reasoning with Tree-of-Mixed-Thought

The field of artificial intelligence (AI) is advancing quickly, with Large Language Models (LLMs) like ChatGPT making strides in understanding and generating language, reasoning, and more.

Yet, these models face challenges in creating long-range plans for complex tasks that involve multiple steps of reasoning based on visual information.

The Tree-of-Mixed-Thought methodology emerges as a cutting-edge solution that blends quick decision-making with thorough, step-by-step reasoning to improve visual reasoning tasks.

The Challenge at Hand

Current LLMs often falter when they need to devise complex, multi-step plans based on visuals. This gap in their capabilities limits their effectiveness in tasks that require detailed reasoning.

What is Tree-of-Mixed-Thought?

This innovative method combines fast, instinctive thinking with a slower, more deliberate thought process called the Tree-of-Thought (ToT).

This blend aims to quicken the pace at which AI can generate plans without compromising the depth of reasoning, especially in visual reasoning scenarios.

Key Features

• Data and Testing: Uses enhanced PTR and CLEVR datasets designed for testing multi-hop reasoning, with a variety of question types and reasoning challenges.
• Planning Strategies:
• ToT-One-Stop (ToT-OS): Quickly concludes the planning process when an initial plan fits the requirements, ensuring speed and efficiency.
• ToT-Block: For more complex planning needs, it creates detailed multi-step plans at every stage, reducing the total number of steps needed.

Testing and Outcomes

Comparative tests against other methods showed Tree-of-Mixed-Thought's superior performance in both speed and accuracy. It confirmed the advantage of merging fast and slow thinking for complex reasoning tasks.

Evaluation Highlights

The approach not only outperformed others in effectiveness but also required fewer steps to reach a conclusion, an essential feature for real-world applications where time and precision matter.

Future Directions

Tree-of-Mixed-Thought marks a significant leap forward in AI, particularly for multi-hop visual reasoning.

By marrying quick and in-depth reasoning processes, it presents a balanced approach to overcoming current limitations. This methodology's potential for enhancing LLMs in various complex reasoning tasks is vast and promising.

As AI continues to evolve, methods like Tree-of-Mixed-Thought are crucial for pushing boundaries and solving more sophisticated problems. The journey towards more advanced, intuitive AI is ongoing, with this approach paving the way.

Further Exploration

For a deeper understanding of Tree-of-Mixed-Thought and its foundational concepts, the original research paper is an excellent resource.

Additionally, exploring recent works on LLMs and hybrid cognitive models in AI can provide more insights into this rapidly evolving field.