Are Smarter LLMs Safer? Exploring Safety-Reasoning Trade-offs in Prompting and Fine-Tuning
Journal:
arXiv
Published Date:
Feb 13, 2025
Abstract
Large Language Models (LLMs) have demonstrated remarkable success across
various NLP benchmarks. However, excelling in complex tasks that require
nuanced reasoning and precise decision-making demands more than raw language
proficiency--LLMs must reason, i.e., think logically, draw from past
experiences, and synthesize information to reach conclusions and take action.
To enhance reasoning abilities, approaches such as prompting and fine-tuning
have been widely explored. While these methods have led to clear improvements
in reasoning, their impact on LLM safety remains less understood. In this work,
we investigate the interplay between reasoning and safety in LLMs. We highlight
the latent safety risks that arise as reasoning capabilities improve, shedding
light on previously overlooked vulnerabilities. At the same time, we explore
how reasoning itself can be leveraged to enhance safety, uncovering potential
mitigation strategies. By examining both the risks and opportunities in
reasoning-driven LLM safety, our study provides valuable insights for
developing models that are not only more capable but also more trustworthy in
real-world deployments.
