新闻
Historical Backtesting for Scientific Question Discovery: A Protocol and Astronomy Pilot
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers formalized historical backtesting to evaluate AI systems that generate scientific research questions, testing whether generated questions were later answered in real literature.
- 为何重要
- Matters for engineers building AI systems for scientific discovery who need objective evaluation beyond subjective expert scoring or LLM-as-judge ratings.
- 注意
- Low inter-rater agreement on outcome taxonomy even among humans suggests the evaluation framework itself needs refinement, not just the judge models.
收听本摘要
- llm
- eval
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