Uncovering Errors and Achieving Record Accuracy - SmartGPT on MMLU + Exam

📈 SmartGPT achieved a new record of 88.4% on the MMLU benchmark.

🧪 The experiments revealed mistakes in an official benchmark used by OpenAI and Google.

💡 The video explores how to benefit from the experiments, particularly in unexpected domains like medicine.

📚 The video discusses the importance of the concept of AGI and achieving high scores on benchmarks.

💡 By using prompt engineering, SmartGPT can reach a performance of 90-92 on the MMLU benchmark.

🧠 The paper explores the limitations of language models like GPT4 in answering questions that require deeper thought or calculation.

🧠 The video discusses the limitations of auto grading and the importance of hand grading answers.

💡 A paper on self-consistency explains that the highest probability answer may not always be the best answer.

📊 Using a larger number of samples can significantly affect the final results of the model.

🎯 The researchers discovered numerous errors in the test, which impacted the final results.

🚀 Using a thread and sync I/O based approach, simultaneous calls to the API at different levels led to significant iteration speed boosts.

💯 The SmartGPT innovation improved GPT 3.5's performance by 3.7% and outperformed the Open AI benchmark on a representative subset of questions.

💡 SmartGPT is a flexible system that can be applied to various domains, and ongoing improvements are being made to enhance its effectiveness.

🔍 There were numerous factual errors found in the sources used for the exam questions.

✅ The Model's Many Logical Units (MMLU) provided incorrect answers to several questions across different subjects.

🔀 The MMLU also mixed up the order of options, resulting in incorrect answers.

🔄 Models trained on the MMLU benchmark will be imitating incorrect reasoning due to compromised answers.

🧩 The MMLU contained misspellings, grammatical ambiguity, and formatting issues.

❓ There were questions with no clear answer or ambiguous options.

📚 GPT 4's answer to complex and controversial questions is more nuanced than GPT 3.

🔢 The video highlights the importance of reducing inaccuracies in AI models when aiming for high levels of accuracy.

📊 There is a need for an independent professional benchmarking organization to develop comprehensive and unambiguous benchmarks for AI models.

💡 Practical components, such as questions on managing equipment in a bio lab, should be included in AI benchmarks.

📈 Google Gemini will draw on five times the compute of GPD4, making benchmarking more urgent.

💡 The video demonstrates how adding exemplars, self-consistency, and self-reflection can improve the performance of GPT4 in medical diagnosis.

🌟 While GPD4 is not recommended for medical diagnoses, the methods used in this process can be applied to diverse domains to push model performance closer to their limits.