Débloquez tout le potentiel de GPT-4 avec l'Arbre des Pensées
Table of Contents
📚 Table of Contents
- Introduction
- GPT4: The Next Step in Language Models
- The Limitations of Token Prediction
- 3.1. Complexity in Answering Complicated Questions
- The Power of Prompting
- 4.1. The IO Method: A Common but Ineffective Prompting Technique
- 4.2. Chain of Thought: Introducing Intermediary Thoughts
- 4.3. SC Chain of Thought: Voting for the Best Path
- 4.4. TOT: Thinking Outside the Linear Box
- TOT: The Four Steps of Thought Processes
- 5.1. Step 1: Thoughts Decomposition
- 5.2. Step 2: Thought Generation
- 5.3. Step 3: State Evaluation
- 5.4. Step 4: Search Algorithm Selection
- Testing the Effectiveness of TOT
- 6.1. The Game of 24: A Mathematical Reasoning Challenge
- 6.2. Creative Writing: Unleashing the Power of Imagination
- 6.3. Mini Crossword Puzzle: Unlocking Language Model as a Problem Solver
- Conclusion
- FAQs
📝 Article
📚 Introduction
With the latest research from Google DeepMind, GPT4, the advanced language model, has achieved a significant milestone in its ability to think. While large language models have impressed us with what they can generate, they have often fallen short in complex reasoning tasks. However, this newly published paper introduces a promising solution that involves the technique of prompting. In this article, we will explore how TOT (Thought-Output-Transformation) has revolutionized the way language models, like GPT4, approach complex tasks.
📚 GPT4: The Next Step in Language Models
Before delving into the advancements brought by the TOT framework, let's understand why traditional methods, such as token prediction, fail to address the complexity of human-like thinking. While large language models astound us with their output, their ability to tackle intricate questions leaves much to be desired. GPT4 aims to bridge this gap and provide language models with the capability to think beyond simple token predictions.
📚 The Limitations of Token Prediction
3.1. Complexity in Answering Complicated Questions
Asking a large language model a complex question reveals the limitations of token prediction methods. The lack of a comprehensive thinking approach hampers the model's ability to arrive at the correct answer. In essence, these models struggle to display the complex thinking Patterns that come naturally to humans. However, the introduction of the TOT framework promises to change this Scenario.
📚 The Power of Prompting
4.1. The IO Method: A Common but Ineffective Prompting Technique
The commonly used IO method, where a task instruction is inputted to the model, often falls short in producing optimal results. For instance, instructing the model, "Teach me how to code," may not yield satisfactory programming lessons. This prompting technique lacks the finesse required for complex thinking.
4.2. Chain of Thought: Introducing Intermediary Thoughts
In contrast, the Chain of Thought technique demonstrates an improvement over the IO method. By introducing intermediary thoughts in the prompt, such as "Do this step by step," or providing examples, ChatGPT is compelled to bridge the gap between input and output. This bridge is created through a series of connected thoughts, leading to a more coherent answer. However, there is still room for error if a mistake occurs at any step of the thought process.
4.3. SC Chain of Thought: Voting for the Best Path
The SC Chain of Thought takes the intermediary thought concept a step further. By creating multiple chains of thought, the language model is empowered to vote for the best path or thought. This method allows for a more strategic approach but retains a linear thinking process. While it represents an improvement, it lacks the capability to Backtrack and explore alternative paths.
4.4. TOT: Thinking Outside the Linear Box
The TOT framework is the key to unlocking a language model's decision-making capabilities. Unlike previous techniques, TOT allows the model to make decisions, explore different paths, and backtrack if necessary. TOT operates in four essential steps: Thoughts Decomposition, Thought Generation, State Evaluation, and Search Algorithm Selection. Let's dive into these steps to understand the comprehensive thinking process of TOT.
📚 TOT: The Four Steps of Thought Processes
5.1. Step 1: Thoughts Decomposition
In this initial step, TOT breaks down complex ideas into smaller components and organizes them in a ranking structure. Each piece becomes a node in a tree, forming relationships and dependencies. This breakdown helps analyze the logical progression of an idea, preparing the model for deeper thinking in subsequent steps.
5.2. Step 2: Thought Generation
Once the thoughts are decomposed and organized, the Thought Generation process begins. The language model generates potential thoughts that act as intermediate steps towards solving the problem at HAND. These thoughts are Based on the model's understanding of the problem and represent partial solutions.
5.3. Step 3: State Evaluation
In Step 3, the language model evaluates different states or partial solutions to determine their usefulness in solving the problem. The State Evaluator assigns a value to each state, reflecting its progress towards the solution. This evaluation allows the model to decide which states to explore further, optimizing the problem-solving Journey.
5.4. Step 4: Search Algorithm Selection
The final step involves selecting the appropriate search algorithm to explore the tree of thoughts. Different search algorithms can be employed depending on the structure of the tree and the nature of the problem. Examples include breadth-first or depth-first search algorithms that strategically look ahead and potentially revisit previous steps to ensure the best possible solution.
📚 Testing the Effectiveness of TOT
6.1. The Game of 24: A Mathematical Reasoning Challenge
The research team tested TOT's capabilities by challenging GPT4 with different tasks. One such task was the mathematical reasoning challenge known as the Game of 24. The objective was to use four numbers and basic arithmetic operations to obtain the number 24. While the IO, cot, and cotsc prompting methods had low success rates (4% to 9%), TOT achieved an impressive success rate of up to 74%.
6.2. Creative Writing: Unleashing the Power of Imagination
Another task involved creative writing, where the language model had to generate a coherent passage using four random input sentences. Humans preferred TOT over cot in 41 out of 100 passages, and 38 out of 100 found them similar. TOT showcased its ability to think creatively and plan strategically, surpassing the other prompting techniques.
6.3. Mini Crossword Puzzle: Unlocking Language Model as a Problem Solver
The researchers also explored the potential of language models as problem solvers through a mini crossword puzzle. Unlike the previous tasks, solving the puzzle required exploratory thinking and systematic planning. While cot prompting struggled with a success rate below 16%, TOT significantly improved the metrics, achieving a word-level success rate of 60% and solving 4 out of 20 games.
📚 Conclusion
The introduction of the TOT framework brings us closer to achieving language models that can think and reason like humans. By enabling comprehensive thought processes and decision-making capabilities, TOT represents a significant step forward in the evolution of language models. These advancements can revolutionize various domains, from mathematics to creative writing, opening up possibilities for even broader applications.
📚 FAQs
Q: What is TOT?
A: TOT (Thought-Output-Transformation) is a framework that enhances the thinking capabilities of language models, such as GPT4. It enables the model to decompose complex thoughts, generate intermediate steps, evaluate state progress, and select search algorithms to arrive at comprehensive solutions.
Q: How does TOT differ from other prompting techniques?
A: TOT surpasses traditional techniques like the IO method, Chain of Thought, and SC Chain of Thought by introducing decision-making capabilities and the ability to explore alternative paths. It offers a more comprehensive and efficient approach to problem-solving.
Q: What tasks did TOT perform better in?
A: TOT showcased superior performance in tasks like the mathematical reasoning challenge, creative writing, and mini crossword puzzles. It outperformed other prompting methods in terms of success rates, coherent writing, and problem-solving efficiency.