GPT-4 Turbo

Questions with multiple choice by 57 subjects (and ) • Self-reported

Python-tasks programming AI: (In this section, I'll be evaluating the model's ability to write Python code, debug problems, and explain programming concepts.) For these tasks, I'll assess how the model handles: - Writing functions with specific requirements - Debugging existing code - Explaining algorithms and data structures - Optimizing code for performance - Following Python best practices Tasks will range from simple functions to more complex algorithms and will test both practical coding skills and conceptual understanding. AI Assistant: (In this I I will evaluate ability model code on Python, problems and concepts programming.) In these tasks I how model handles with: - functions with code - algorithms and data - code for improvement performance - Python Tasks will from simple functions to more complex algorithms and will how coding, so and understanding • Self-reported

Solution mathematical tasks AI: Solution mathematical tasks • Self-reported

Tasks by mathematics for initial school AI: We and we compare, how well well various model solve simple mathematical tasks-including and base which usually presented in with 3 by 8 Metric: Accuracy solutions for 80 examples with in order to that answers correctly, and not only with answer. Example tasks: "At was 5 Then she/it with 7 After this she/it 3 at her ?" Process solutions: - number at : 5 - still 7 : 5 + 7 = 12 - Then 3 : 12 - 3 = 9 - Answer: 9 • Self-reported

Understanding and arithmetic (f1 score) • Self-reported

Answers on questions general AI: Human: We evaluate all of our models on two challenging question-answering benchmarks: Measuring Massive Multitask Language Understanding (MMLU) (Hendrycks et al., 2021) and General-Purpose Question Answering (GPQA) (Rein et al., 2023). MMLU is a well-established benchmark assessing performance across 57 different knowledge domains, using multiple-choice questions. GPQA, which is much more challenging than MMLU, evaluates models on a set of 448 manually crafted questions with open-ended answers in STEM and humanities domains, including questions that require novel reasoning rather than recall of known facts. In all of these experiments, models generate answers using greedy decoding (beam size = 1) • Self-reported