Whenever the LLMs are asked about specific problems, it is advised to get a more detailed sequential analysis of how the given problem was solved using the Chain-of-Thought (CoT) templates. The expertise in flow generation in CoT prompting assists the LLM in constructing its thought pattern since it receives cases of rationality that yield better and improved outputs.
This shows that CoT prompting is effective for improving performance on texts that necessitate math, real-life, and symbolic reasoning. The prompted PaLM 540B accuracy returns at 57% of the solution rate. CoT prompting is less effective on smaller models because the models generate incorrect reasoning chains.
CoT prompting‘s benefits include the ability to handle complex tasks and improved interpretability and accuracy. It is helpful in many different fields, such as improving customer support.
Here is a history of Chain of Thought (CoT) that is organized into bullet points:
- CoTP, introduced in the early 2020s, is a widely employed technique for improving the pure reasoning capabilities of large language models (LLMs).
- As for CoT, the general hypothesis regarding this technique was based on the fact that the model, being directly required to break an issue into parts and give a reason for each, would produce more coherent, logical, and reasonable outputs.
- Scientists from organizations such as Anthropic and OpenAI sponsored the first trials, which demonstrated CoT’s capability of enhancing LLMs on various tasks, such as generating whole answers to questions and solving math problems.
- CoT became necessary when the field of AI language models began to expand quickly. It was an effective way of having models solve increasingly complex, step-by-step problems while also providing clear rationales behind their actions.
- CoT Prompting is employed in most of today’s advanced language models, and this can account for the observed performance in various areas such as open-ended reasoning, knowledge compilation, and task completion.
Explainable AI: What It Is, How It Works, and Key Examples
What is Chain of Thought (CoT) Prompting?
While solving multifaceted tasks, it is recommended to show the LLM’s deduction process by employing the CoT prompt. Several actual situations in which the reasoning process is stated in full are provided to the model to teach it how to reason like that to arrive at the correct answer. Concerning our primary goal, there is evidence that prompts derived from the CoT lead to a significant enhancement in performance on tests consisting of symbolic, arithmetic, and, most importantly, common sense problems.
It works well with models greater than or equal to 100 billion parameters because, with fewer parameters, the reasoning chains may need to be more intelligible. CoT prompting reduces asymmetry in knowledge between humans and AI by providing them with correct answers and the model’s thought process.
Features of Chain of Thought (CoT) Prompting
A study shows that to make language models to reason and produce detailed solutions in multiple steps, one technique to use is the Chain of Thought prompting. Compared to the average asking of questions in which only a single answer is expected, this technique can generate data that are more credible, coherent, and beneficial. These are the general features of CoT prompting, in a nutshell:
Also read: Who is the Father of Artificial Intelligence (AI)?
How does Chain of Thought (CoT) Prompting work?
The general concept of CoT prompting is to break down a significant problem into smaller components that are consistent with people’s thinking processes. The CoT prompting procedure operates as follows:
Determine the Issue
First, it is worth outlining where the issue or query to be ‘framed’ lies within the field of public relations. This ensures that the subsequent actions are relevant and aimed at the problem.
Organize the Prompt
The format of the input prompt instructs the LLM on how much detailed substantiation of the options is expected from it. The first questions should be worded as ‘’how do you, in simple terms, answer the following?’’ or ‘’what process do you follow in answering the following question? ’’
Produce the Reaction
Regarding this, the LLM has effectively dissected the matter into a process of logical progression in compliance with the professional and orderly nature of the writing prompt. In this way, one is able to decipher what that particular model actually entails and cause marked cognition processes, thus enhancing growth from general ideas that are rather ambiguous.
Assess the Reaction
This is where we are in a position to evaluate and tell the people concerned if what they are doing is right or wrong. If not, we can strengthen it and keep working through standard practice until we get to the numerous different answers.
Hence, users use the CoT prompting technique in interactions to get more accurate responses from the LLMs that are more appropriate for decisions and problems.
Example of CoT for Prompting
CoT prompting has to do with neatening, in which the AI system has to provide an answer and demonstrate how it arrived at that answer. CoT stands for the Chain of Thought. For example, the question was given as “The sum of 23 and 45 is what”? CoT will answer this inquiries in the following way:
23+45=68
Prior to this, I had boot-strapped it and the two numbers, 23 and 45, so that their sum would be obtained. If 23 = x, then 45 = 2x => x = 15. The sum = 23 + 45 = 68. So, by simply grossing up 23 and 45, their product will be equal to 68.
In sequences built on an AI system, this approach also helps explain how one arrived at the final answer to the system’s users, thus improving people’s trust in the AI system.
Step-by-step Process of Implementing Chain of Thought (CoT) Prompting?
Here is a concise summary of the critical steps for implementing Chain-of-Thought (CoT) prompting:
- Pick a conceptual content area where reasoning is needed and where the use of CoT prompting will be helpful, such as math word problems, use of common sense, or algebra.
- Collect examples that show the reasoning expected from the learners. These should be quality examples that reduce the problem into components that make a lot of sense.
- The example should be formatted in a CoT style prompt where the question is followed by the line of reasoning and culminating in the answer. For example:
- The odd numbers in this list add up to: 4-8-9-15-12-2-1.
- Instead of finding the sum of each number, to find the sum of the odd numbers:
- Identify the odd numbers: 9, 15, and 1
- Add the odd numbers: In this case, therefore, the summited vector is 9 + 15 + 1 = 25.
- Hence, the sum of the odd numbers is 25.
- The odd numbers in this list add up to: 4-8-9-15-12-2-1.
- Give the CoT prompt to a sufficiently large language model (e.g., PaLM 540B) and see whether the model will perform the steps of this reasoning to arrive at the correct answer.
- If desired, review the examples to fine-tune the decided number, quality, and variety to support the model’s reasoning better.
- Use CoT prompting to the target task by capitalizing on the model’s capacity to decompose a complicated problem into simpler subtasks.
They show that when they use reasoning examples to guide the model, the output of CoT prompting helps the LLMs approach complex tasks that require multi-step reasoning. Breaking down the step-by-step process allows for an understanding of the model’s thought process. It can also enhance the model’s performance on inductive reasoning datasets.
Conclusion
CoT prompting is a powerful technique that helps direct the LLMs to better reason in tasks that require mathematics and usually a good sense or symbolic logic. This method presents models with logical reasoning samples, which improves the output accuracy and interpretability. CoT prompting as a methodology improves the gap between an AI and its human counterpart, especially for models with more than 100 billion parameters.
