Assigning higher redundancy to semantically important query features reduces retrieval error probability under token erasures, via multivariate Gaussian approximations of similarity margins and supporting numerical results.
Retrieving and reading: A comprehensive survey on open-domain question answering
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LaMDA shows that fine-tuning on human-value annotations and consulting external knowledge sources significantly improves safety and factual grounding in large dialog models beyond what scaling alone achieves.
RADS applies reinforcement learning to pick informative samples for transfer learning, improving performance over uncertainty and diversity sampling in low-resource imbalanced clinical settings.
A survey that organizes temporal question answering research via a unified view of corpus temporality, question temporality, and model capabilities while reviewing neural, transformer, and LLM advances plus benchmarks.
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Context-Aware Search and Retrieval Under Token Erasure
Assigning higher redundancy to semantically important query features reduces retrieval error probability under token erasures, via multivariate Gaussian approximations of similarity margins and supporting numerical results.
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LaMDA: Language Models for Dialog Applications
LaMDA shows that fine-tuning on human-value annotations and consulting external knowledge sources significantly improves safety and factual grounding in large dialog models beyond what scaling alone achieves.
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RADS: Reinforcement Learning-Based Sample Selection Improves Transfer Learning in Low-resource and Imbalanced Clinical Settings
RADS applies reinforcement learning to pick informative samples for transfer learning, improving performance over uncertainty and diversity sampling in low-resource imbalanced clinical settings.
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It's High Time: A Survey of Temporal Question Answering
A survey that organizes temporal question answering research via a unified view of corpus temporality, question temporality, and model capabilities while reviewing neural, transformer, and LLM advances plus benchmarks.