In the ever-evolving world of artificial intelligence, Large Language Models (LLMs) are at the forefront of transforming how we interact with technology. From generating human-like text to solving complex problems, these models push the boundaries of what’s possible. But how do we measure their performance and capabilities? Enter the world of benchmarks. Let's explore key benchmarks for LLMs, what they measure, and why they matter. A "shot" refers to the number of examples provided to the model before it attempts to perform a task. 0-shot : The model is given no examples and must rely on its pre-existing knowledge. Few-shot (like 5-shot or 8-shot) : Before attempting the task, the model is provided with a few examples (5 or 8) to learn from. This helps the model better understand the task format and context. "Shots" gauge how well a model can generalize knowledge and apply it to new situations with varying degrees of prior information. General Benchmarks
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