  Recent studies have demonstrated that In-Context Learning (ICL), through the
use of specific demonstrations, can align Large Language Models (LLMs) with
human preferences known as In-Context Alignment (ICA), indicating that models
can comprehend human instructions without requiring parameter adjustments.
However, the exploration of the mechanism and applicability of ICA remains
limited. In this paper, we begin by dividing the context text used in ICA into
three categories: format, system prompt, and example. Through ablation
experiments, we investigate the effectiveness of each part in enabling ICA to
function effectively. We then examine how variants in these parts impact the
model's alignment performance. Our findings indicate that the example part is
crucial for enhancing the model's alignment capabilities, with changes in
examples significantly affecting alignment performance. We also conduct a
comprehensive evaluation of ICA's zero-shot capabilities in various alignment
tasks. The results indicate that compared to parameter fine-tuning methods, ICA
demonstrates superior performance in knowledge-based tasks and tool-use tasks.
However, it still exhibits certain limitations in areas such as multi-turn
dialogues and instruction following.
