Clinical Machine Learning at University Hospital Zurich / He, Him
Joined October 2015
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*The meaning of prompts* (2)
With regards to soft-prompt tuning, Khashabi et al. (2022) found that good-performing soft prompts can be mapped to arbitrary text as the mapping between continuous and discrete spaces is *not* one-to-one.
*The meaning of prompts* (1)
Webson and Pavlick (2022) state that it is unlikely that a "models’ improvement is derived from models understanding task instructions in ways analogous to humans’ use of task instructions"
*Correct labeling is less important*
Min et al. (2022, EMNLP) show that replacing gold labels with random labels only marginally hurts performance; trend is consistent over nearly all models tested and independent of prompt template
*Various biases lead to high result variance*
Zhao et al. (2021) demonstrate that result variance can be caused by phenomena such as recency bias, majority label bias and common token bias
*Results are sensitive to order*
As shown by Lu et al. (2022) and Zhao et al. (2021), in-context example order has a strong influence on the few-shot performance and there seems to be an “optimal” order of in-context examples (Kumar and Talukdar, 2022)
