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In-context learning has been applied to knowledge-rich tasks such as questionanswering. In such scenarios, in-context examples are used to trigger abehaviour in the language model: namely, it should surface information storedin its parametric knowledge. We study the construction of in-context examplesets, with a focus on the parametric knowledge of the model regardingin-context examples. We identify 'known' examples, where models can correctlyanswer from its parametric knowledge, and 'unknown' ones. Our experiments showthat prompting with 'unknown' examples decreases the performance, potentiallyas it encourages hallucination rather than searching its parametric knowledge.Constructing an in-context example set that presents both known and unknowninformation performs the best across diverse settings. We perform analysis onthree multi-answer question answering datasets, which allows us to furtherstudy answer set ordering strategies based on the LM's knowledge about eachanswer. Together, our study sheds lights on how to best construct in-contextexample sets for knowledge-rich tasks.

Crafting In-context Examples according to LMs' Parametric Knowledge