Learning to Defer
PulseAugur coverage of Learning to Defer — every cluster mentioning Learning to Defer across labs, papers, and developer communities, ranked by signal.
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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 optimizes LLM use for extractive question answering
Researchers have developed a Learning-to-Defer framework to improve the efficiency of extractive question answering (EQA) using large language models. This method intelligently allocates queries to specialized models, e…
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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…