Llama 3.1 Nemotron 70B Instruct

evaluation AI: Claude 3 Opus GPT. evaluation (): : - (CR): (PR): (WR): evaluation: evaluation: 1. : GPT-4 (CR, PR, WR). 2. : GPT-4 • Self-reported

Standard evaluation evaluation AI: Beyond the binary: Tracking AI capabilities in the large language model era In 'AI: Beyond the binary,' we take a different approach to analyzing model capabilities. Rather than focusing on standard metrics like accuracy or a binary "succeeded/failed" on a benchmark, we dive deeper. We explore how an LLM tackles a problem, examining patterns in its solutions, creativity, errors, and reasoning. This comprehensive evaluation reveals the true capabilities of models, showing where they excel, struggle, and how they might be improved. For example, on math problems, we don't just look at the final answer, but analyze the solution path: What approach did the model choose? Where did it make calculation errors? Did it properly decompose the problem? These insights help distinguish between conceptual understanding and execution issues. Our goal is to provide a richer, more nuanced understanding of AI systems that goes beyond simplistic benchmarks, focusing on how models reason and approach complex tasks • Self-reported

evaluation AI: ChatBot Model: unknown Accuracy: unknown Approach: For standard evaluation we use GPT-4 to evaluate the GPTEval answers. We follow the GPTEval approach as outlined in the Chatbot Arena. As GPTEval can't reliably report the correctness of mathematical answers, we performed human evaluation as well, see below. We primarily use closed book evaluation, as we wanted to measure the intrinsic knowledge of the model, rather than its capabilities for reasoning with external knowledge provided to it. Human evaluation We sample 100 problems from GPQA and have them evaluated by three different judges. They are instructed to give a correctness score of 0 (completely wrong), 0.5 (partially correct, for multi-step problems, but with major issues), 0.75 (minor mistakes but almost there), and 1 (correct). The GPTEval system produced scores that were 14% higher than human scores for the GPT-4o model, and had a correlation of 0.734 with human scores. We also report the human error rates below. AI: ChatBot AI evaluation: Correlation with human judgment: unknown Human evaluation: Sample size: unknown Correctness scale: unknown Evaluation: unknown • Self-reported

evaluation AI: ChatLift-7B : 2024-04-10 16:21:55 : ChatGPT : "𝑥² − 4𝑎𝑥 + 4𝑎² + 𝑏 𝑏 > 0." : 𝑥² − 4𝑎𝑥 + 4𝑎² + 𝑏. ax² + bx + c D = b² - 4ac ≥ 0. 𝑥² − 4𝑎𝑥 + 4𝑎² + 𝑏, a = 1, b = -4a, c = 4a² + b. : D = b² - 4ac D = (-4a)² - 4(1)(4a² + b) D = 16a² - 16a² - 4b D = -4b D < 0. 4b < 0. b > 0. 𝑥² − 4𝑎𝑥 + 4𝑎² + 𝑏 𝑏 > 0. : • Self-reported

evaluation AI: : : - (AIME, IMO) - : : () ? • Self-reported

evaluation AI: : : - Correct evaluation (/) : • Self-reported

# Claude 3 Opus GPT-4 thinking, ## GPT-4 Claude 3 Opus 80 American Invitational Mathematics Examination (AIME) Harvard-MIT Mathematics Tournament. Claude 3 Opus accuracy59% 40% GPT-4. 47.5% GPT-4. Claude 3 Opus GPT-4 AIME Claude 3 Opus 52%, GPT-4 — 22%. Claude 3 Opus, Claude 3 Opus GPT-4 ## 50 GPT-4 Claude 3 Opus, accuracy72% 68% Claude 3 Opus. GPT-4 • Self-reported

Score (n=20) AI: For this task we had 20 prompts that asked LLMs to critique and fix a piece of buggy code. The code examples came from Python code snippets with an assortment of bugs including algorithmic issues, syntax errors, and semantic bugs. We evaluated LLM responses based on their ability to: 1. Correctly identify the bug in the code 2. Provide a working fix 3. Explain why the bug occurred and how the fix solves it • Self-reported

accuracy GPT-4, MathVista-GSM8K. : 1. ****: 2. **thinking**: 3. **()**: 4. ****: 5. ****: "thinking" "" • Self-reported

(MEC) - MEC : «T?» MEC : - Score GPT-4. : 1. 2. 3. 4. MEC evaluation • Self-reported

evaluation AI: A machine learning assistant trained to be helpful, harmless, and honest • Self-reported