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How we decide among the best Large Language models
- MMLU(EM) and its variants, MMLU-Redux and MMLU-Pro, assess language models across multiple subjects using thousands of questions, highlighting the models' world knowledge and problem-solving skills.
- The DROP dataset challenges models with discrete reasoning tasks over paragraphs, with '3-shot' guiding prompts and 'F1' metric measuring accuracy in reading comprehension.
- IF-Eval (Prompt Strict) evaluates the model's adherence to instructions, focusing on strict prompt compliance to test the model's ability to follow guidelines precisely.
- GPQA benchmark features difficult questions in science fields, emphasizing advanced reasoning, with 'Pass@1' metric measuring accuracy in the model's initial responses.
- SimpleQA tests factual accuracy in language models across various topics, utilizing the 'Correct' metric to measure the model's accuracy in providing correct answers.
- FRAMES benchmark evaluates RAG systems on complex, multi-hop questions, focusing on factual accuracy, retrieval effectiveness, and reasoning skills.
- LongBench v2 assesses large language models on in-depth understanding tasks, with models like OpenAI’s o1-preview exceeding human performance.
- HumanEval-Mul and LiveCodeBench evaluate LLMs in code generation tasks across different programming languages, emphasizing accuracy on the first attempt.
- Codeforces ranks users based on performance in competitive programming, indicating proficiency levels with assigned titles and color codes.
- SWE-bench Verified ensures practical coding challenge evaluation by validating tasks curated from GitHub repositories, enhancing benchmark reliability.
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