The Art of Defending: A Systematic Evaluation and Analysis of LLM
  Defense Strategies on Safety and Over-Defensiveness
Neeraj Varshney Pavel Dolin Agastya Seth Chitta Baral
As Large Language Models (LLMs) play an increasingly pivotal role in natural
language processing applications, their safety concerns become critical areas
of NLP research. This paper presents Safety and Over-Defensiveness Evaluation
(SODE) benchmark: a collection of diverse safe and unsafe prompts with
carefully designed evaluation methods that facilitate systematic evaluation,
comparison, and analysis over 'safety' and 'over-defensiveness.' With SODE, we
study a variety of LLM defense strategies over multiple state-of-the-art LLMs,
which reveals several interesting and important findings, such as (a) the
widely popular 'self-checking' techniques indeed improve the safety against
unsafe inputs, but this comes at the cost of extreme over-defensiveness on the
safe inputs, (b) providing a safety instruction along with in-context exemplars
(of both safe and unsafe inputs) consistently improves safety and also
mitigates undue over-defensiveness of the models, (c) providing contextual
knowledge easily breaks the safety guardrails and makes the models more
vulnerable to generating unsafe responses. Overall, our work reveals numerous
such critical findings that we believe will pave the way and facilitate further
research in improving the safety of LLMs.