How Far Can In-Context Alignment Go? Exploring the State of In-Context Alignment
This work addresses the problem of aligning LLMs with human preferences without parameter adjustments, offering insights for researchers and practitioners, though it is incremental in exploring ICA's mechanisms and applicability.
The paper investigates In-Context Alignment (ICA) for aligning Large Language Models with human preferences using demonstrations, finding that examples are crucial for enhancing alignment and that ICA outperforms fine-tuning in knowledge-based and tool-use tasks but has limitations in multi-turn dialogues and instruction following.
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.