297 citations · 314 across the 8 of their papers we have counts for
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cs.CL2023★ 8 cited
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
Han Zhou, Xingchen Wan, Lev Proleev +4
Prompting and in-context learning (ICL) have become efficient learning paradigms for large language models (LLMs). However, LLMs suffer from prompt brittleness and various bias fac…
cs.LG2023★ 1 cited
Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression
Alexander Norcliffe, Lev Proleev, Diana Mincu +3
Multiple sclerosis is a disease that affects the brain and spinal cord, it can lead to severe disability and has no known cure. The majority of prior work in machine learning for m…