[ML News] GPT-4: Solving MIT Exam with 100% Accuracy

Table of Contents

1. Introduction
2. The Controversial Paper on MIT Mathematics and EECS Curriculum using Large Language Models 2.1 Data Collection 2.2 Prompt Engineering 2.3 Finding Good Prompts 2.4 Chaining from Zero-shot Learning to Few-shot Learning 2.5 Expert Prompting 2.6 Fine-tuning and Automatic Grading 2.7 Results and Criticisms
3. Open Llama: A Reproduction Project

The Controversy Surrounding MIT Mathematics and EECS Curriculum Evaluation Using Large Language Models

The recent publication of a paper exploring the evaluation of MIT mathematics and EECS (Electrical Engineering and Computer Science) curriculum using large language models has sparked controversy and raised questions about the validity of the findings. In this article, we will Delve into the details of this controversial paper and the criticisms it has faced.

1. Introduction

The paper in question focuses on evaluating the capabilities of GPT-4, a large language model, in solving a comprehensive set of 4550 questions and corresponding solutions from 30 MIT mathematics and EECS courses. The paper claims that GPT-4, with prompt engineering and other techniques, achieves a perfect solve rate on a test set that excludes image-Based questions.

2. The Controversial Paper on MIT Mathematics and EECS Curriculum using Large Language Models

2.1 Data Collection

The authors of the paper collected a vast amount of data consisting of questions and tasks from the MIT mathematics and EECS curriculum. They employed a semi-automated process to extract the Relevant content, correcting any errors made by the extraction process.

2.2 Prompt Engineering

Prompt engineering is a crucial aspect of the evaluation process. The authors implemented various techniques, including zero-shot learning, few-shot learning, expert prompting, critique, and tree search. These techniques were used to prompt GPT-4 and guide its problem-solving process.

2.3 Finding Good Prompts

Finding effective Prompts is vital for obtaining accurate results. The authors outlined their approach for finding good prompts, starting with zero-shot learning to determine if a question can be solved directly. If not, they employed few-shot learning to search for similar questions and their corresponding solutions. They also explored techniques such as Chain of Thought prompting and expert prompting.

2.4 Chaining from Zero-shot Learning to Few-shot Learning

A significant aspect of the evaluation process involved chaining from zero-shot learning to few-shot learning. This approach allowed GPT-4 to attempt solving a question using different techniques iteratively. If an answer was incorrect, GPT-4 would move to the next technique in the hierarchy.

2.5 Expert Prompting

Expert prompting was introduced as a Novel contribution in the evaluation process. The authors asked GPT-4 to name three experts who would be most suitable for answering a given question. This role-play aspect aimed to improve the quality of the answers by incorporating the knowledge and expertise of renowned figures.

2.6 Fine-tuning and Automatic Grading

The authors employed fine-tuning on an open-source model specific to the problem set. However, this did not significantly impact the results they reported. Automatic grading played a crucial role in the evaluation, where the provided answer was compared to a gold standard solution. This process aimed to determine the correctness of the response.

2.7 Results and Criticisms

The paper presents the results, claiming that GPT-4 achieved a perfect solve rate on the test set, excluding image-based questions. However, several concerns and criticisms have been raised regarding the methodology employed and the validity of the results. The presence of unsolvable questions in the test set, duplicates in the data, questionable prompts, and the cascading approach to solving questions have all raised doubts about the accuracy and integrity of the findings.

3. Open Llama: A Reproduction Project

In a separate development, researchers at Berkeley have undertaken the Open Llama project. The project aims to reproduce the Llama model using the Red Pajama dataset. The recently released 13B version of the Open Llama model shows promising results and has been trained on one trillion tokens. The project's open nature and commitment to reproducibility are highly commendable.

Overall, the controversial paper on evaluating the MIT mathematics and EECS curriculum using large language models raises important questions about the methodology and reliability of such studies. It serves as a reminder to approach research findings with a critical mindset and to encourage the replication of experiments to validate and verify results.

Highlights

• A controversial paper on evaluating the MIT mathematics and EECS curriculum using large language models has sparked concerns and criticisms.
• The paper claims that GPT-4 achieved a perfect solve rate on a test set of 4550 questions, excluding image-based questions.
• The data collection process involved extracting questions and tasks using a semi-automated approach.
• Prompt engineering techniques, such as zero-shot learning, few-shot learning, expert prompting, and critique, were used to guide GPT-4 in problem-solving.
• The paper has been criticized for including unsolvable questions in the test set, duplicates in the data, questionable prompts, and the cascading approach to solving questions.
• The Open Llama project aims to reproduce the Llama model using the Red Pajama dataset, demonstrating a commitment to open and reproducible research.

FAQ

Q: What is the controversial paper about? A: The paper explores the evaluation of the MIT mathematics and EECS curriculum using large language models, specifically focusing on GPT-4.

Q: What techniques were used in the evaluation process? A: The evaluation process involved prompt engineering techniques such as zero-shot learning, few-shot learning, expert prompting, critique, and tree search.

Q: What are the criticisms raised against the paper? A: Some criticisms include the inclusion of unsolvable questions in the test set, duplicates in the data, questionable prompts, and the cascading approach to solving questions.

Q: What is the Open Llama project? A: The Open Llama project is a reproduction project undertaken by researchers at Berkeley, aiming to reproduce the Llama model using the Red Pajama dataset.

Q: How reliable are the results presented in the paper? A: The validity of the results has been brought into question due to various concerns and criticisms regarding the methodology employed in the evaluation process.