Fundamental safety-capability trade-offs in fine-tuning large language models.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Natural Language Processing

Background:

• Large language models (LLMs) are commonly fine-tuned on task-specific datasets to enhance capabilities.
• Empirical evidence shows that fine-tuning often compromises LLM safety, creating a safety-capability trade-off.

Purpose of the Study:

• To present a theoretical framework for understanding the safety-capability trade-off in LLM fine-tuning.
• To analyze the impact of data similarity, context overlap, and alignment loss landscape on this trade-off.

Main Methods:

• Development of a theoretical framework to analyze safety-aware LLM fine-tuning strategies.
• Investigation of the interplay between safety and capability through data similarity and context overlap metrics.
• Validation of theoretical findings using numerical experiments.

Main Results:

• The study provides new insights into how data similarity and context overlap affect the safety-capability trade-off.
• Theoretical results characterize the fundamental limits of compromising safety when enhancing LLM capabilities.
• Numerical experiments validate the theoretical framework and its predictions.

Conclusions:

• The safety-capability trade-off in LLM fine-tuning is a fundamental challenge.
• Understanding the influence of data characteristics and loss landscapes is crucial for developing safer LLMs.
• The proposed framework offers a basis for mitigating safety risks during LLM capability enhancement.