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

2025-02-17

AI Summary

Existing LLM alignment methods rely heavily on high-quality human annotations and extensive computational resources. To address this, we propose a fine-tuning-free, low-resource alignment enhancement paradigm. Our key insight is the identification of *language style* as a critical latent variable governing alignment performance—previously unexplored in alignment research. Building upon this, we introduce a style-rewriting framework that explicitly reconstructs the linguistic expression of high-quality in-context examples to jointly optimize the inherently conflicting objectives of factual consistency and safety. The method integrates style-aware in-context example rewriting, multi-objective prompt composition, and zero-/few-shot alignment triggering mechanisms. Evaluated on Alpaca, Just-Eval, and MT-Bench, our approach achieves absolute improvements of +0.10, +0.22, and +0.32 (out of 5.00), respectively, surpassing state-of-the-art baselines. All code and data are publicly released.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Enhance LLM alignment via restyled ICL exemplars.
• Address factuality and safety balance in LLMs.
• Improve ethical behavior using low-cost, tuning-free methods.

Innovation

• Restyles ICL exemplars stylistically
• Balances factuality and safety aspects
• Uses restyled prompts for few-shot learning