Two new arXiv papers explore methods to reduce human intervention in online classification tasks, particularly for large language models. The first paper introduces the Conservative Hull-based Classifier (CHC) and Generalized Hull-based Classifier (GHC) to minimize regret in active learning frameworks by maintaining convex hulls of labeled data. The second paper proposes a no-regret approach for safe online classification in medical screening, focusing on minimizing costly tests while adhering to error constraints. AI
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IMPACT These papers offer theoretical advancements in reducing the cost of human feedback for AI systems, potentially impacting the efficiency of training and fine-tuning models.
RANK_REASON Two academic papers published on arXiv detailing new methods for online classification and active learning.