RIDE: Enhancing Large Language Model Alignment through Restyled In-Context Learning Demonstration Exemplars

TL;DR

This paper introduces RIDE, a low-cost, tuning-free in-context learning method that enhances large language model alignment by restyling exemplars based on stylistic analysis, improving performance on multiple benchmarks.

Contribution

It proposes a novel style-based restyling approach for in-context learning exemplars to improve LLM alignment without additional training.

Findings

Achieves up to 0.32 point improvements on benchmarks

Enhances LLM alignment by restyling exemplars based on style

Provides a low-cost, tuning-free alternative to alignment methods

Abstract

Alignment tuning is crucial for ensuring large language models (LLMs) behave ethically and helpfully. Current alignment approaches require high-quality annotations and significant training resources. This paper proposes a low-cost, tuning-free method using in-context learning (ICL) to enhance LLM alignment. Through an analysis of high-quality ICL demos, we identified style as a key factor influencing LLM alignment capabilities and explicitly restyled ICL exemplars based on this stylistic framework. Additionally, we combined the restyled demos to achieve a balance between the two conflicting aspects of LLM alignment--factuality and safety. We packaged the restyled examples as prompts to trigger few-shot learning, improving LLM alignment. Compared to the best baseline approach, with an average score of 5.00 as the maximum, our method achieves a maximum 0.10 increase on the Alpaca task (from 4.50 to 4.60), a 0.22 enhancement on the Just-eval benchmark (from 4.34 to 4.56), and a maximum improvement of 0.32 (from 3.53 to 3.85) on the MT-Bench dataset. We release the code and data at https://github.com/AnonymousCode-ComputerScience/RIDE.