How Far Can In-Context Alignment Go? Exploring the State of In-Context Alignment

TL;DR

This paper investigates the mechanisms and effectiveness of In-Context Alignment (ICA) in Large Language Models, highlighting the importance of examples and evaluating ICA's capabilities across different tasks and limitations.

Contribution

The study systematically analyzes the components of ICA, demonstrating the critical role of examples and providing a comprehensive evaluation of ICA's zero-shot performance.

Findings

Examples significantly influence alignment performance

ICA outperforms fine-tuning in knowledge and tool-use tasks

Limitations remain in multi-turn dialogues and instruction following

Abstract

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.