Guidance with Chain of Thought in Artificial Intelligence
Posted: Mon Dec 09, 2024 6:14 am
Artificial intelligence (AI) systems have been advancing at an accelerating pace in recent years, achieving human-level performance in many areas such as image classification, speech recognition, and language translation. However, when it comes to more complex reasoning abilities, today’s AI still falls short of the versatile and adaptable human mind. This new technique, called “Chain of Thought Prompting,” aims to advance AI systems’ reasoning capabilities by showing them step-by-step reasoning processes.
What is Chain of Thought Prompting?
It is a method that allows an AI to create a step-by-step explanation by connecting them in a logical and sequential manner so that it can arrive at an answer. This method provides a series of consistent intermediate reasoning steps by creating examples that demonstrate solving a problem through a “chain of thought.”
The method here is actually quite simple; give examples that will show the AI the paths that will lead it to the answer.
For example: If Ezgi has 12 apples and gives 8 of them to her friend, how many apples does she have left? Let's look at an example thought process of the AI to solve the question:
1- Ezgi had 12 apples.
2- Ezgi gave 8 apples to her friend.
3- So 12 - 8 = 4 apples left.
4- Therefore, the answer is 4.
With a few examples, AI can also create its own thinking system overseas chinese in uk data for new problems. Researchers who tested this on several major language models, including PaLM, LaMDA, and GPT-3, have reached striking conclusions:
- Math problems: Thought chain guidance completed the task by increasing the solution rates from 33% to 57% on difficult problems.
- Common sense reasoning: He continued to improve his performance in common sense reasoning such as evaluating history or sports events.
- Symbolic reasoning: He was able to generalize from short examples because he learned reasoning patterns in longer tests.
The findings of these studies are as follows:
- The larger the model you work with, the more consistent reasoning chains you can produce, which means making human-like inferences.
- This method is especially successful in complex and multi-step requests.
- Can continue learning in a way that is not affected by minor differences in examples or explanations.
Benefits of Chain of Thought Prompting
Improves understanding by breaking down complex processes into intermediate steps.
It provides interpretability and systematicity to the thinking process of artificial intelligence.
It adapts to reasoning methods such as common sense and symbolism.
It can add reasoning capabilities to already trained language models with minimal training.
Can generate more ideas by brainstorming.
Although we have discussed promising developments, there are still unanswered questions. How scalable can reasoning skills be to larger language models and more representations? Can this approach extend beyond logic to causal reasoning? How reliably can these language models reason and make sense of real-world events? Can AIs also learn to spontaneously generate chains of thought?
With the right training, AI could have the capacity to create solutions for a variety of situations it has never seen before, just as humans can reason about new problems using logic.
What is Chain of Thought Prompting?
It is a method that allows an AI to create a step-by-step explanation by connecting them in a logical and sequential manner so that it can arrive at an answer. This method provides a series of consistent intermediate reasoning steps by creating examples that demonstrate solving a problem through a “chain of thought.”
The method here is actually quite simple; give examples that will show the AI the paths that will lead it to the answer.
For example: If Ezgi has 12 apples and gives 8 of them to her friend, how many apples does she have left? Let's look at an example thought process of the AI to solve the question:
1- Ezgi had 12 apples.
2- Ezgi gave 8 apples to her friend.
3- So 12 - 8 = 4 apples left.
4- Therefore, the answer is 4.
With a few examples, AI can also create its own thinking system overseas chinese in uk data for new problems. Researchers who tested this on several major language models, including PaLM, LaMDA, and GPT-3, have reached striking conclusions:
- Math problems: Thought chain guidance completed the task by increasing the solution rates from 33% to 57% on difficult problems.
- Common sense reasoning: He continued to improve his performance in common sense reasoning such as evaluating history or sports events.
- Symbolic reasoning: He was able to generalize from short examples because he learned reasoning patterns in longer tests.
The findings of these studies are as follows:
- The larger the model you work with, the more consistent reasoning chains you can produce, which means making human-like inferences.
- This method is especially successful in complex and multi-step requests.
- Can continue learning in a way that is not affected by minor differences in examples or explanations.
Benefits of Chain of Thought Prompting
Improves understanding by breaking down complex processes into intermediate steps.
It provides interpretability and systematicity to the thinking process of artificial intelligence.
It adapts to reasoning methods such as common sense and symbolism.
It can add reasoning capabilities to already trained language models with minimal training.
Can generate more ideas by brainstorming.
Although we have discussed promising developments, there are still unanswered questions. How scalable can reasoning skills be to larger language models and more representations? Can this approach extend beyond logic to causal reasoning? How reliably can these language models reason and make sense of real-world events? Can AIs also learn to spontaneously generate chains of thought?
With the right training, AI could have the capacity to create solutions for a variety of situations it has never seen before, just as humans can reason about new problems using logic.