  Large language models (LLMs) enable in-context learning (ICL) by conditioning
on a few labeled training examples as a text-based prompt, eliminating the need
for parameter updates and achieving competitive performance. In this paper, we
demonstrate that factual knowledge is imperative for the performance of ICL in
three core facets: the inherent knowledge learned in LLMs, the factual
knowledge derived from the selected in-context examples, and the knowledge
biases in LLMs for output generation. To unleash the power of LLMs in few-shot
learning scenarios, we introduce a novel Knowledgeable In-Context Tuning (KICT)
framework to further improve the performance of ICL: 1) injecting knowledge
into LLMs during continual self-supervised pre-training, 2) judiciously
selecting the examples for ICL with high knowledge relevance, and 3)
calibrating the prediction results based on prior knowledge. We evaluate the
proposed approaches on autoregressive models (e.g., GPT-style LLMs) over
multiple text classification and question-answering tasks. Experimental results
demonstrate that KICT substantially outperforms strong baselines and improves
by more than 13% and 7% on text classification and question-answering tasks,
respectively.
