Learning to Defer
PulseAugur coverage of Learning to Defer — every cluster mentioning Learning to Defer across labs, papers, and developer communities, ranked by signal.
3 天有情绪数据
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新框架优化LLM在抽取式问答中的使用
研究人员开发了一个学习延迟（Learning-to-Defer）框架，以提高使用大型语言模型（LLM）进行抽取式问答（EQA）的效率。该方法智能地将查询分配给专用模型，确保高置信度的预测，同时最大限度地降低计算成本。该框架在SQuADv1和TriviaQA等数据集上进行了测试，证明了其提高了答案的可靠性并显著降低了计算开销，使其适用于可扩展的EQA部署。
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New Learning-to-Defer methods leverage expert advice and multi-expert collaboration
Researchers have developed new methods for 'Learning-to-Defer' (L2D) systems, which decide whether to make a prediction or consult an expert. The latest advancements address limitations in existing frameworks by allowin…
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New framework reframes Learning to Defer via density-ratio estimation
Researchers have introduced a novel post-hoc Learning to Defer (L2D) framework that reframes the problem through the lens of ideal distributions. This approach defines deferral by calculating the density-ratio between a…
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New online algorithm enhances Learning-to-Defer with dynamic experts
Researchers have developed a new online algorithm for Learning-to-Defer (L2D) methods, designed to handle streaming data and dynamic expert availability. This algorithm is the first of its kind for multiclass classifica…