Task Verification and LLM Judge Alignment#A key concern in synthetic data generation is label quality: if supporting documents do not actually support the clues, or distractors inadvertently contain the answer, training signal degrades. Simply asking a model to score a document as relevant can be unreliable, and human labeling is costly since it requires reading each document thoroughly. We overcome these challenges with an extraction-based verification pipeline.
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当前市场以电动球包车、遥控运输设备等半自动化产品为主。具备"感知+理解+决策"能力的真正机器人尚未出现。行业存在三类割裂工具：解决体力劳动的球包车、测量数据的设备、提供基础信息的轻量工具，它们都缺乏真正的智能决策能力。，详情可参考豆包下载
// In a crate that depends on `log`
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