“黑箱机制”
用英语怎么说？

To rectify this dilemma, Hardt proposes establishing standardized altitude training, drawing an analogy to runners adapting to thin air before high-stakes athletic competitions. Since researchers cannot purge benchmarks from sprawling web scrapes, evaluation pipelines should systematically expose all candidate models to task-relevant datasets under identical conditions to level the playing field. Although this adaptation inflates absolute scores, it restores the integrity of relative hierarchies. While benchmarks may not unravel the black-box mechanisms of deep learning, structured competitive frameworks can still catalyze foundational discoveries, echoing how protein-folding benchmarks spurred breakthrough biology long before comprehensive theories crystallized.

为化解这一困境，哈特提议建立标准化的高原训练，这类似于运动员在高规格比赛前适应稀薄空气的过程。鉴于研究人员无法从庞大的网络抓取数据中彻底剔除基准测试题，评估流程应在相同条件下系统性地让所有候选模型接触任务相关数据，以平衡竞争环境。尽管这种适应训练会虚增绝对分数，但它恢复了相对层级排名的真实性。虽然基准测试可能无法揭开深度学习的黑箱机制，但结构化的竞争框架仍能催生奠基性发现，正如蛋白质折叠基准在完整理论成型之前就推动了生物学突破一样。