Defending large language models (LLMs) against jailbreak attacks is crucial for ensuring their safe deployment. Existing defense strategies generally rely on predefined static criteria to differentiate between harmful and benign prompts. However, such rigid rules are incapable of accommodating the inherent complexity and dynamic nature of real jailbreak attacks. In this paper, we propose a novel concept of ``mirror'' to enable dynamic and adaptive defense. A mirror refers to a dynamically generated prompt that mirrors the syntactic structure of the input while ensuring semantic safety. The personalized discrepancies between the input prompts and their corresponding mirrors serve as the guiding principles for defense. A new defense paradigm, MirrorGuard, is further proposed to detect and calibrate risky inputs based on such mirrors. An entropy-based detection metric, Relative Input Uncertainty (RIU), is integrated into MirrorGuard to quantify the discrepancies between input prompts and mirrors. MirrorGuard is evaluated on several popular datasets, demonstrating state-of-the-art defense performance while maintaining general effectiveness.

Computational narrative is a complex and interesting domain for exploring AI techniques that algorithmically analyze, understand, and most importantly, generate stories. This paper studies the importance of domain knowledge in story generation, and particularly in analogy-based story generation (ASG). Based on the construct of knowledge container in case-based reasoning, we present a theoretical framework for incorporating domain knowledge in ASG. We complement the framework with empirical results in our existing system Riu.

A key challenge in computational narrative is story generation. In this paper we focus on analogy-based story generation, and, specifically, on how to generate both story and text using analogy. We present a dual representation formalism where a human-understandable representation (composed of English sentences) and a computer-understandable representation (consisting in a graph) are linked together in order to generate both story and natural language text by analogy. We have implemented our technique in the Riu interactive narrative system.