PAL improves few-shot reasoning accuracy by having LLMs generate executable programs rather than text-based chains of thought, outperforming much larger models on math and logic benchmarks.
arXiv preprint arXiv:2012.05876 , year=
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Independently trained PPG and accelerometer health foundation models share a linearly alignable subspace in which health-condition classifiers transfer with >95% of in-domain AUC.
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PAL: Program-aided Language Models
PAL improves few-shot reasoning accuracy by having LLMs generate executable programs rather than text-based chains of thought, outperforming much larger models on math and logic benchmarks.
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Emergent Symbolic Structure in Health Foundation Models: Extraction, Alignment, and Cross-Modal Transfer
Independently trained PPG and accelerometer health foundation models share a linearly alignable subspace in which health-condition classifiers transfer with >95% of in-domain AUC.