Comprehensive Evaluation of LLM Performance

Large Language Model (LLM) evaluation benchmarks are essential for assessing LLM performance across various tasks. They consist of standardized datasets, tests, and metrics. Key benchmarks include:

1. Abstraction and Reasoning Corpus (ARC): Evaluates abstract reasoning through 1,000 visual puzzles.
2. Bias Benchmark for QA (BBQ): Analyzes social biases in question-answering with 58K multiple-choice questions.
3. BIG-Bench Hard (BBH): A suite of 23 challenging tasks measuring diverse reasoning skills.
4. BoolQ: Tests entailment with 15,942 yes/no questions.
5. DROP: Assesses reading comprehension via complex questions, using 6,700 paragraphs.
6. EquityMedQA: Focuses on biases in medical QA with 1,871 questions.
7. GSM8K: Evaluates multi-step arithmetic with 8,500 grade-school problems.
8. HellaSwag: Commonsense reasoning benchmark with 10K sentence completions.
9. HumanEval: Tests functional Python code generation with 164 challenges.
10. IFEval: Measures instruction-following across 500 prompts.
11. LAMBADA: Requires context understanding with 10K narrative passages.
12. LogiQA: Evaluates logical reasoning with 8,678 question pairs.
13. MathQA: Focuses on math problem-solving with 37K questions.
14. MMLU: Comprehensive multitask evaluation with nearly 16K questions.
15. SQuAD: Assesses reading comprehension with over 100K pairs from Wikipedia.
16. TruthfulQA: Tests factual accuracy with 817 diverse questions.
17. Winogrande: Evaluates commonsense reasoning with about 44K binary questions.

These benchmarks are vital for refining LLM capabilities.

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