The paper "tinyBenchmarks: evaluating LLMs with fewer examples" by Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, and Mikhail Yurochkin addresses the challenge of evaluating the performance of large language models (LLMs) on diverse benchmarks, which typically consist of tens of thousands of examples, making the process computationally and financially expensive. The authors propose strategies to reduce the number of examples needed for accurate evaluation while maintaining reliability and efficiency. Key contributions include: 1. **Evaluation Strategies**: The paper explores three main strategies for selecting a subset of examples: stratified random sampling, clustering based on model correctness, and Item Response Theory (IRT). These strategies aim to identify representative examples that can be used to estimate the performance of LLMs. 2. **IRT-Based Methods**: The authors introduce IRT-based methods to enhance performance estimation. These methods leverage the IRT model to create meaningful representations of examples, allowing for more robust and accurate performance estimates. 3. **TinyBenchmarks**: Based on the evaluation strategies, the authors release tiny versions of popular benchmarks (Open LLM Leaderboard, MMLU, HELM, and AlpacaEval 2.0), each containing only 100 curated examples per scenario. These tiny versions are designed to be efficient and reliable for evaluating LLMs. 4. **Empirical Analysis**: Extensive empirical analysis demonstrates that the proposed evaluation strategies and tiny benchmarks are effective in reliably and efficiently reproducing the original evaluation results. The average estimation error is under 2% across all benchmarks and evaluated LLMs. The paper concludes that with 100 curated examples per scenario, it is possible to accurately assess the capabilities of LLMs, reducing evaluation costs significantly. The release of tinyBenchmarks and evaluation tools aims to facilitate more frequent and efficient evaluation of LLMs in various applications.The paper "tinyBenchmarks: evaluating LLMs with fewer examples" by Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, and Mikhail Yurochkin addresses the challenge of evaluating the performance of large language models (LLMs) on diverse benchmarks, which typically consist of tens of thousands of examples, making the process computationally and financially expensive. The authors propose strategies to reduce the number of examples needed for accurate evaluation while maintaining reliability and efficiency. Key contributions include: 1. **Evaluation Strategies**: The paper explores three main strategies for selecting a subset of examples: stratified random sampling, clustering based on model correctness, and Item Response Theory (IRT). These strategies aim to identify representative examples that can be used to estimate the performance of LLMs. 2. **IRT-Based Methods**: The authors introduce IRT-based methods to enhance performance estimation. These methods leverage the IRT model to create meaningful representations of examples, allowing for more robust and accurate performance estimates. 3. **TinyBenchmarks**: Based on the evaluation strategies, the authors release tiny versions of popular benchmarks (Open LLM Leaderboard, MMLU, HELM, and AlpacaEval 2.0), each containing only 100 curated examples per scenario. These tiny versions are designed to be efficient and reliable for evaluating LLMs. 4. **Empirical Analysis**: Extensive empirical analysis demonstrates that the proposed evaluation strategies and tiny benchmarks are effective in reliably and efficiently reproducing the original evaluation results. The average estimation error is under 2% across all benchmarks and evaluated LLMs. The paper concludes that with 100 curated examples per scenario, it is possible to accurately assess the capabilities of LLMs, reducing evaluation costs significantly. The release of tinyBenchmarks and evaluation tools aims to facilitate more frequent and efficient evaluation of LLMs in various applications.