In the previous topics, we discussed advanced prompt engineering methods such as Chain of Thought prompting, zero-shot Chain of Thought, self-consistency, and applied programming. Now, we will examine how to apply these techniques in solving multiple choice questions using chatGPT.
The Chain of Thought technique is a method that can be used to guide chatGPT toward a specific answer to a multiple choice question. It involves creating a series of questions that build upon each other to guide the AI toward the correct answer. Here’s an example to help illustrate the technique:
Let’s say the multiple choice question is: ‘What is the capital of Egypt?’ and the answer choices are: Cairo, Alexandria, Luxor, and Aswan. To use the Chain of Thought technique to answer this question, we could start with a question that provides a clue to the answer, such as ‘What is the largest city in Egypt?’ The AI could respond with ‘Cairo’. From there, we could ask a follow-up question like ‘What is the capital of a country?’ and the AI would respond with ‘The capital is the city or town that serves as the seat of government for a country.’ We could then ask ‘What is the seat of government in Egypt?’ and the AI would respond with ‘Cairo’. Finally, we could ask ‘So, what is the capital of Egypt?’ and the AI would answer ‘Cairo’, which is the correct answer to the original multiple choice question.
The Chain of Thought technique is especially effective for questions that have a clear logical connection between the clues and the correct answer.
Zero-shot Chain of Thought is a technique that involves using a prompt to guide chatGPT to a specific answer, even if the AI has no prior knowledge of the subject matter. This can be useful when dealing with multiple choice exam questions that require specific information to arrive at the correct answer. For example, if the AI is asked a multiple choice question about a specific historical event, a zero-shot Chain of Thought prompt could start with a question like ‘What year did the event occur?’ This information can then be used to narrow down the possible answers and arrive at the correct answer. This technique relies on chatGPT’s ability to use language to reason and infer, allowing it to make educated guesses based on the information provided in the prompt.
While it may not always lead to the correct answer, zero-shot Chain of Thought can be an effective way to approach certain types of multiple choice questions that require a specific piece of information to arrive at the correct answer.
Self-consistency is another technique that chatGPT can use to solve multiple choice exam questions. With this technique, chatGPT is asked to provide an explanation for its answer, and then the explanation is verified to be consistent with the answer. For example, suppose we ask chatGPT a multiple choice question about a specific scientific principle. A self-consistency prompt could ask chatGPT to explain how the principle works. If the explanation is consistent with the answer, it is likely that chatGPT has arrived at the correct answer. This technique is effective because it helps to ensure that chatGPT is not just guessing the answer, but actually understands the underlying concept or principle. By asking chatGPT to provide an explanation, we can also gain insights into how the AI arrived at its answer, which can be helpful for improving its performance in the future.
Self-consistency can be used for a wide range of multiple choice exam questions and is especially useful for questions that require a deep understanding of a particular subject matter.
Applied programming is a more complex technique that involves writing custom code to solve specific types of multiple choice exam questions. This method can be particularly useful for questions that require complex calculations or involve specific formulas. For example, consider a math problem that asks for the value of a complex function given certain inputs. An applied programming approach could involve writing a custom program to evaluate the function and determine the correct answer. This technique requires a high level of technical expertise and can be time-consuming to implement. However, it can be very effective for solving specific types of multiple choice exam questions where other methods may not be sufficient.
In conclusion, chatGPT holds immense potential to revolutionize the way multiple choice exams are created and graded. With its advanced language processing capabilities, it can quickly and accurately analyze questions and provide appropriate answers. Several techniques, such as Chain of Thought, zero-shot Chain of Thought, self-consistency, and applied programming, can utilize the power of chatGPT to navigate complex thought processes and arrive at the correct answers. By leveraging chatGPT, educators and examiners can streamline the exam creation process and ensure accurate grading. This could significantly improve the efficiency and fairness of the assessment process, ultimately benefiting students and institutions alike. Overall, the potential benefits of chatGPT for multiple choice exams are vast, and it will be exciting to see how this technology continues to evolve and transform the education sector.
