Benchmarking three large language model tasks (GSM8K, HellaSwag, and TruthfulQA) on a single T4 GPU costs approximately $0.12. The analysis reveals that generative tasks are the primary cost driver, while log-likelihood tasks can be processed in parallel. Optimizing by capping tokens at 256, using a 25% stratified sample, and employing MC2 scoring can significantly reduce runtime and costs. AI
影响 Provides a cost breakdown for LLM evaluation, suggesting methods to reduce expenses for researchers and developers.
排序理由 Analysis of computational costs for LLM evaluation benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →