GPT5 unlocks LLM System 2 Thinking?

AI has come a long way in recent years, with large language models (LLMs) like GPT-4 impressing us with their ability to generate text. However, these models primarily rely on system one thinking, which is fast and intuitive but lacks the ability to break down complex problems into smaller steps and explore different options. This limitation has led researchers to focus on developing GPT-5 with enhanced reasoning abilities and reliability.

The Two Modes of Thinking

In his book "Thinking, Fast and Slow," Daniel Kahneman introduces the concept of two modes of thinking: system one and system two. System one thinking is our fast, intuitive brain that quickly provides answers based on memorized information. On the other hand, system two thinking is slower but more rational, requiring us to take time, calculate, and analyze before arriving at an answer.

Similarly, large language models like GPT-4 primarily rely on system one thinking. They predict the best next words based on the sequence of words they have seen before, without truly understanding the complex problems they are trying to solve.

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The Limitations of GPT-4

GPT-4, despite its impressive capabilities, lacks system two thinking. It cannot break down complex tasks into smaller steps or explore different options. It simply generates text based on patterns it has learned from training data. This limitation becomes evident when GPT-4 is faced with complex problems that require deeper analysis and reasoning.

For example, in a video by Veritasium, college students were asked seemingly simple questions like the time it takes for the Earth to go around the Sun. Many of them answered incorrectly because they relied on system one thinking, providing automatic intuitive answers without truly considering the question.

Large language models like GPT-4 face a similar challenge. They lack the ability to think critically and break down complex problems into smaller, manageable steps. This is where GPT-5 comes in.

The Promise of GPT-5

GPT-5 aims to enhance the reasoning abilities of large language models and introduce system two thinking. OpenAI's Sam Altman mentioned in an interview with Bill Gates that the key milestones for GPT-5 will be around reasoning ability and reliability.

Currently, GPT-4 can reason in extremely limited ways and lacks reliability. It may provide correct answers, but it doesn't always know which answer is the best. GPT-5 aims to improve this by increasing reliability and enhancing reasoning abilities.

Altman also mentioned the possibility of GPT-5 being able to solve complex math equations by applying transformations an arbitrary number of times. This would require a more complex control logic for reasoning, going beyond what is currently possible with GPT-4.

However, simply improving the model itself is not enough. There are ways to enforce system two thinking in large language models today, even with GPT-4.

Promoting System 2 Thinking in Large Language Models

There are two common strategies to promote system two thinking in large language models: prompt engineering and communicative agents.

Prompt Engineering

Prompt engineering is a simple and common method to guide large language models towards system two thinking. One approach is the "chain of thought," where a sentence is inserted step by step before the model generates any text. This forces the model to break down the problem into smaller steps and think through each one.

Another approach is to provide a few short prompt examples instead of a step-by-step process. These examples guide the model towards thinking through different steps and considering multiple possibilities.

While prompt engineering can be effective in promoting system two thinking, it has limitations. It often restricts the model to consider only one possibility and may not explore diverse options, similar to how humans approach creative problem-solving.

To address this limitation, more advanced prompting tactics like self-consistency with chain of thought (SCCOT) have been proposed. SCCOT involves running the chain of thought process multiple times and reviewing and voting on the most reasonable answers. This allows for some exploration of different options but requires more implementation effort.

Another advanced prompting tactic is the tree of sorts, which simulates a tree search to explore different options and paths. It keeps track of all the paths explored and allows for backtracking if the current path doesn't lead to the desired outcome. However, implementing the tree of sorts is complex and requires significant implementation effort.

Communicative Agents

Communicative agents provide an elegant solution to promote system two thinking in large language models. These are multi-agent setups where users can define different agents and simulate conversations between them. The agents can reflect and spot flaws in each other's perspectives and thinking processes.

Communicative agents have shown promise in enhancing system two thinking. They allow for dedicated agents to review and critique the model's answers, identifying flaws and providing feedback. This collaborative approach mimics how humans solve complex problems by exploring multiple options and learning from each other.

Setting up communicative agents can be done using various frameworks like ChatGPT, MetaGPT, Autogen, and Crew AI. These frameworks enable the creation of agent workflows and facilitate conversations between agents with different roles, such as problem solvers and reviewers.

Autogen Studio, a no-code interface for Autogen, simplifies the setup of communicative agent workflows. It allows for easy collaboration and problem-solving between agents, making it accessible to a wider range of users.

Unlocking System 2 Thinking with GPT-4 Today

While GPT-4 may not have native system two thinking capabilities, prompt engineering and communicative agents can be used to enforce system two thinking and solve complex tasks.

Prompt engineering, such as the chain of thought or self-consistency with chain of thought, guides the model towards thinking through problems step by step and considering multiple possibilities. However, prompt engineering may limit exploration and diversity of solutions.

Communicative agents, on the other hand, provide a collaborative approach to problem-solving. By simulating conversations between agents, users can leverage the strengths of system one and system two thinking. Reviewers can spot flaws in the model's answers, while problem solvers can iterate and improve their solutions based on feedback.

Frameworks like Autogen Studio make it easy to set up communicative agent workflows, allowing for seamless collaboration and problem-solving.

The Future of GPT-5 and System 2 Thinking

GPT-5 holds the promise of unlocking system two thinking in large language models. With enhanced reasoning abilities and reliability, GPT-5 aims to bridge the gap between system one and system two thinking, enabling models to solve complex problems more effectively.

Researchers are actively working on developing GPT-5 with improved reasoning abilities. The focus is on enabling large language models to break down complex tasks, explore different options, and make more accurate and informed decisions.

As we look forward to the advancements in GPT-5, it's important to continue exploring and implementing strategies like prompt engineering and communicative agents to drive system two thinking in large language models today.

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FAQs

1. Can GPT-4 solve complex problems?

GPT-4 can generate text and provide answers, but it primarily relies on system one thinking. It lacks the ability to break down complex problems into smaller steps and explore different options.

2. How can prompt engineering promote system two thinking?

Prompt engineering, such as the chain of thought or self-consistency with chain of thought, guides large language models towards thinking through problems step by step and considering multiple possibilities.

3. What are communicative agents?

Communicative agents are multi-agent setups where users can define different agents and simulate conversations between them. This allows for collaborative problem-solving and promotes system two thinking.

4. How can communicative agents be set up?

Frameworks like Autogen Studio provide a no-code interface for setting up communicative agent workflows. Users can define agents, assign roles, and simulate conversations to solve complex problems.

5. What is the future of GPT-5?

GPT-5 aims to enhance reasoning abilities and bridge the gap between system one and system two thinking. It holds the promise of enabling large language models to solve complex problems more effectively.