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Jerry Liu谈基准测试分类:前沿智能与专用智能各有侧重

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LlamaIndex 创始人把基准分成两类讲得很清楚,还解释了为啥自家要做 ParseBench 和 ExtractBench,做文档处理的可以看看怎么给自己选基准。

LlamaIndex 创始人 Jerry Liu 提出基准测试可分为两类:一类是 TerminalBench、GDPval 这类衡量前沿智能的基准,准确率优先于成本;另一类是衡量专用智能的基准,重点看成本与准确率的帕累托曲线。他指出前沿模型在高算力档位上往往已能在第二类基准上饱和,但价格让人难以接受。LlamaIndex 为文档任务自建了 ParseBench 和 ExtractBench,并推出更难的 ExtractBench Pro,开始同时投入两类基准的研究。

原文 · Jerry Liu

There are two main types of benchmarks: 1. those that measure frontier intelligence (terminalbench, gdpval, etc.). accuracy matters more than cost. 2. those that measure specialized intelligence. the pareto curve of cost/accuracy more than accuracy alone. both are important, but they are distinct benchmark categories. * as (1) saturates, they become (2). * the largest frontier models at xhigh likely already saturate (2), but they do so at a cost/latency point that's completely price-prohibitive. * every user should define (2) for the tasks they care about the most. we're big fans of both. we have so far dedicated outsized attention to (2) especially related to document tasks. we fundamentally think users should have the most price-efficient tools to understand, extract, and read over their documents. we've built parsebench, extractbench for this purpose. we are starting to invest in (1) as well, because certain document tasks are so challenging that even frontier models struggle (e.g. see extractbench pro). we are continuing our investment in long-horizon workflow research at @llama_index 💬 5 🔄 0 ❤️ 8 👀 1010 📊 7 ⚡

  • Ethan Mollick11-02 06:30原文