| license: mit | |
| task_categories: | |
| - text-generation | |
| - question-answering | |
| language: | |
| - en | |
| tags: | |
| - benchmark | |
| - evaluation | |
| - reasoning | |
| - multiple-choice | |
| - llm | |
| size_categories: | |
| - medium | |
| # 📊 Simple Bench Dataset | |
| **A Compact Benchmark for Structured Reasoning and Multiple-Choice Evaluation in Large Language Models** | |
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| <strong><a href="https://huggingface.co/buckets/sapiens-technology/simple_bench/resolve/simple_bench.zip?download=true">Simple Bench Dataset</a></strong> is a structured evaluation collection derived from the Simple Bench benchmark, designed to assess reasoning, comprehension, and multiple-choice question-answering capabilities of large language models through concise yet non-trivial problems that require logical inference rather than simple retrieval; each sample consists of a natural language <em>input</em> containing a question with multiple-choice options (A–F) and an <em>output</em> representing the correct answer, enabling straightforward and deterministic evaluation; the dataset is model-agnostic and optimized for benchmarking performance across reasoning tasks, fine-tuning QA systems, and comparing robustness on short-form logical problems, with evaluation typically performed via exact match accuracy or option-level classification, making it suitable for standardized and reproducible LLM assessment pipelines. | |
| </div> | |
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| <div align="right"> | |
| <sub>Development of Sapiens Technology®️</sub> | |
| </div> | |