Mind the instructions: a holistic evaluation of consistency and
  interactions in prompt-based learning
Lucas Weber Elia Bruni Dieuwke Hupkes
Finding the best way of adapting pre-trained language models to a task is a
big challenge in current NLP. Just like the previous generation of task-tuned
models (TT), models that are adapted to tasks via in-context-learning (ICL) are
robust in some setups but not in others. Here, we present a detailed analysis
of which design choices cause instabilities and inconsistencies in LLM
predictions. First, we show how spurious correlations between input
distributions and labels -- a known issue in TT models -- form only a minor
problem for prompted models. Then, we engage in a systematic, holistic
evaluation of different factors that have been found to influence predictions
in a prompting setup. We test all possible combinations of a range of factors
on both vanilla and instruction-tuned (IT) LLMs of different scale and
statistically analyse the results to show which factors are the most
influential, interactive or stable. Our results show which factors can be used
without precautions and which should be avoided or handled with care in most
settings.