  Just like the previous generation of task-tuned models, large language models
(LLMs) that are adapted to tasks via prompt-based methods like
in-context-learning (ICL) perform well in some setups but not in others. This
lack of consistency in prompt-based learning hints at a lack of robust
generalisation. We here introduce the ICL consistency test -- a contribution to
the GenBench collaborative benchmark task (CBT) -- which evaluates how
consistent a model makes predictions across many different setups while using
the same data. The test is based on different established natural language
inference tasks. We provide preprocessed data constituting 96 different
'setups' and a metric that estimates model consistency across these setups. The
metric is provided on a fine-grained level to understand what properties of a
setup render predictions unstable and on an aggregated level to compare overall
model consistency. We conduct an empirical analysis of eight state-of-the-art
models, and our consistency metric reveals how all tested LLMs lack robust
generalisation.
