Uncovering Errors and Achieving Record Accuracy - SmartGPT on MMLU + Exam
SmartGPT achieves a major benchmark record of 89.0% accuracy on MMLU + Exam, uncovering errors and highlighting the importance of accurate models.
00:00:00 SmartGPT achieves a new unofficial benchmark record of 89.0% on MMLU, revealing mistakes in a globally used benchmark. The video shows how to benefit from the experiments in unexpected domains like medicine.
📈 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.
00:03:54 SmartGPT achieves a benchmark score of 89.0% on MMLU, close to human expert ability. With prompt engineering, it could reach 90-92, and the 95 threshold could be broken by next year. SmartGPT addresses the issue of immediate single character answers, allowing deeper thought for complex questions.
📚 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.
00:07:47 SmartGPT achieves a major benchmark of 89.0% accuracy on MMLU, uncovering errors in the test. Learn key lessons on improving model performance and the importance of exploring the full probability distribution of outputs.
🧠 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.
00:11:39 SmartGPT achieved remarkable performance improvements in benchmark tests, beating previous scores and uncovering errors in exam questions. The system is highly flexible and adaptable, with ongoing improvements planned. It has the potential to handle large-scale data and has applications in various domains.
🚀 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.
00:15:32 The video exposes numerous factual errors in the MMLU benchmark, revealing incorrect answers from various sources, including Oxford University Press. The virology and college chemistry sections are especially problematic. The video also highlights issues with question dependents, ambiguous questions, and unclear answers.
🔍 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.
00:19:25 SmartGPT achieves a benchmark score of 89.0%, highlighting many errors in the exam. It emphasizes the complexity of controversial topics and the need for accurate models at human expert level. A call is made for an independent benchmarking organization.
📚 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.
00:23:20 SmartGPT achieved a major benchmark score of 89.0% on MMLU + Exam and demonstrated its ability to improve performance in medical diagnosis with the use of exemplars, self-consistency, and self-reflection.
📈 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.
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