  The ability of large language models (LLMs) to $``$learn in context$"$ based
on the provided prompt has led to an explosive growth in their use, culminating
in the proliferation of AI assistants such as ChatGPT, Claude, and Bard. These
AI assistants are known to be robust to minor prompt modifications, mostly due
to alignment techniques that use human feedback. In contrast, the underlying
pre-trained LLMs they use as a backbone are known to be brittle in this
respect. Building high-quality backbone models remains a core challenge, and a
common approach to assessing their quality is to conduct few-shot evaluation.
Such evaluation is notorious for being highly sensitive to minor prompt
modifications, as well as the choice of specific in-context examples. Prior
work has examined how modifying different elements of the prompt can affect
model performance. However, these earlier studies tended to concentrate on a
limited number of specific prompt attributes and often produced contradictory
results. Additionally, previous research either focused on models with fewer
than 15 billion parameters or exclusively examined black-box models like GPT-3
or PaLM, making replication challenging. In the present study, we decompose the
entire prompt into four components: task description, demonstration inputs,
labels, and inline instructions provided for each demonstration. We investigate
the effects of structural and semantic corruptions of these elements on model
performance. We study models ranging from 1.5B to 70B in size, using ten
datasets covering classification and generation tasks. We find that repeating
text within the prompt boosts model performance, and bigger models ($\geq$30B)
are more sensitive to the semantics of the prompt. Finally, we observe that
adding task and inline instructions to the demonstrations enhances model
performance even when the instructions are semantically corrupted.
