DECAT classifies multimodal representations into four diagnostic scenarios using null-referenced metrics and a rule-based procedure to detect shared biology versus confounders without knowing the confounder identity.
arXiv preprint arXiv:2306.05268 , year=
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SPIN lets weak LLMs become strong by self-generating training data from previous model versions and training to prefer human-annotated responses over its own outputs, outperforming DPO even with extra GPT-4 data on benchmarks.
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When Are Multimodal Predictions Biologically Supported? A Diagnostic Evaluation Framework
DECAT classifies multimodal representations into four diagnostic scenarios using null-referenced metrics and a rule-based procedure to detect shared biology versus confounders without knowing the confounder identity.
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Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
SPIN lets weak LLMs become strong by self-generating training data from previous model versions and training to prefer human-annotated responses over its own outputs, outperforming DPO even with extra GPT-4 data on benchmarks.