The article argues that the Federal Reserve's recent rate hike decision, while justifiable on inflation grounds, highlights a critical lack of an underlying, transparent policy framework. The core problem is that the Fed's decisions appear discretionary, leading to market uncertainty because the committee's judgment, rather than clear data, dictates policy shifts. The author proposes that the Fed adopt a formal monetary policy rule—an algebraic formula linking the target rate to indicators like inflation and unemployment—to replace subjective guidance. Implementing such a rule would provide market predictability, enhance transparency, and shield the Fed from political attacks by making deviations from the standard easily quantifiable.
Simpler Is Better for Autograders: Toward Cost-Effective LLM Evaluations for Open-Ended Tasks
English Summary
This RAND report addresses the bottleneck of evaluating large language models (LLMs) in open-ended tasks, which is typically constrained by the high cost and slow speed of expert human grading. The analysis tested five autograding methods and found that the simple 'single rubric' approach consistently outperformed complex techniques like metaprompting or prompt optimization. This method achieves a statistically significant reduction in error while matching or exceeding the accuracy of nonexpert human graders, but at a fraction of the time and cost. Policymakers should adopt single-rubric autograders as the default, scalable solution to enable cost-effective and reliable LLM evaluation across diverse domains.
中文摘要
這份RAND報告探討了評估大型語言模型（LLMs）在開放式任務中的瓶頸問題，該瓶頸通常受限於專家人工評分的高成本和低效率。分析測試了五種自動評分方法，發現簡單的「單一評分標準」（single rubric）方法，持續優於諸如元提示（metaprompting）或提示優化（prompt optimization）等複雜技術。此方法在顯著降低錯誤率的同時，其準確性可與非專家人工評分相當甚至超越，但所需的時間和成本卻大大降低。政策制定者應將單一評分標準的自動評分器作為預設的、可擴展的解決方案，以實現跨多領域、具成本效益且可靠的LLM評估。
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