大型语言模型(LLM)的出现,标志着自然语言处理领域取得了重大进展,使我们能够通过自然语言与计算机进行交互。然而,这些模型的评估需要可靠的基准测试,而现有的基准测试却存在着不少问题。
MMLU:一个广受欢迎但存在问题的基准测试
MMLU(Massive Multitask Language Understanding,大规模多任务语言理解)基准测试,因其涵盖了数学、历史、计算机科学、逻辑、法律等多个领域的知识而备受关注。然而,我们发现,尽管MMLU很受欢迎,但它存在着大量错误,这些错误会误导模型评估和比较。
MMLU中的错误:一个需要解决的问题
研究人员发现,MMLU中存在着各种各样的错误,从简单的解析和抓取错误,到更复杂的上下文、解释和数据集质量问题。例如,在病毒学子集中,57% 的问题都存在错误,其中一些错误甚至建议将美军派往西非以阻止埃博拉疫情的爆发。
MMLU-Redux:一个更可靠的基准测试
为了解决MMLU中存在的错误问题,研究人员手动分析了MMLU数据集,并创建了MMLU-Redux。MMLU-Redux 包含3000个经过手动重新标注的问题,涵盖了MMLU的30个子集。研究人员发现,MMLU-Redux 的结果与原始MMLU的评估结果存在显著差异,这表明MMLU中存在的错误对模型评估结果产生了重大影响。
MMLU-Redux:一个更可靠的基准测试
MMLU-Redux 的创建,为我们提供了重新评估LLM性能的工具。研究人员发现,在MMLU-Redux 上,一些LLM的性能表现与原始MMLU评估结果存在显著差异,这表明MMLU中的错误会影响模型的排名。
自动修复MMLU:一个挑战
研究人员还尝试了使用LLM自动修复MMLU中的错误。他们使用了多种方法,包括零样本提示、少样本提示、链式思维提示和检索增强生成。然而,即使是最先进的模型,在自动错误检测方面的表现仍然有限。
结论:MMLU需要改进
MMLU是一个重要的基准测试,但它存在着不少问题。MMLU-Redux 的出现,为我们提供了一个更可靠的基准测试。研究人员呼吁社区共同努力,改进MMLU,使其成为评估下一代LLM的可靠工具。
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