  In-context learning can improve the performances of knowledge-rich tasks such
as question answering. In such scenarios, in-context examples trigger a
language model (LM) to surface information stored in its parametric knowledge.
We study how to better construct in-context example sets, based on whether the
model is aware of the in-context examples. We identify 'known' examples, where
models can correctly answer from their parametric knowledge, and 'unknown'
ones. Our experiments show that prompting with 'unknown' examples decreases the
performance, potentially as it encourages hallucination rather than searching
for its parametric knowledge. Constructing an in-context example set that
presents both known and unknown information performs the best across diverse
settings. We perform analysis on three multi-answer question answering
datasets, which allows us to further study answer set ordering strategies based
on the LM's knowledge of each answer. Together, our study sheds light on how to
best construct in-context example sets for knowledge-rich tasks.
