Large Language Models (LLMs) have been shown to be susceptible to jailbreak attacks, or adversarial attacks used to illicit high risk behavior from a model, highlighting the critical need to safeguard widely-deployed models. Safeguarding approaches, which include fine-tuning models or having LLMs “self-reflect,” may lengthen the inference time of a model, incur a computational penalty, reduce the semantic fluency of an output, and restrict “normal” model behavior. Importantly, these Safety-Performance Trade-offs (SPTs) remain an understudied area. In this work, we make three contributions: (1) We introduce SAFENUDGE, a novel safeguard that combines Controlled Text Generation and “nudging.” SAFENUDGE triggers during text-generation while a jailbreak attack is being executed, and can reduce successful jailbreak attempts by between 28.1% and 37.3% by guiding the LLM towards a safe response. It adds minimal latency to inference and has a negligible impact on the semantic fluency of outputs. Second, it supports tunable SPTs, meaning practitioners can set their own tolerance for trade-offs balancing safety and restrictions to normal model behavior. Third, we release the source code for SAFENUDGE at https://github.com/joaopfonseca/SafeNudge. It is open source and compatible with the HuggingFace transformers library.

Sentence-level claim detection is a critical first step in the fact-checking process. While Large Language Models (LLMs) seem well-suited for claim detection, their computational cost poses challenges for real-world deployment. This paper investigates the effectiveness of both small and large pretrained Language Models for the task of claim detection. We conduct a comprehensive empirical evaluation using BERT, ModernBERT, RoBERTa, Llama, and ChatGPT-based models. Our results reveal that smaller models, when finetuned appropriately, can achieve competitive performance with significantly lower computational overhead on in-domain tasks. Notably, we also find that BERT-based models transfer poorly on sentence-level claim detection in out-of-domain tasks. We discuss the implications of these findings for practitioners and highlight directions for future research.