Is the System Message Really Important to Jailbreaks in Large Language Models?

The rapid evolution of Large Language Models (LLMs) has rendered them indispensable in modern society. While security measures are typically in place to align LLMs with human values prior to release, recent studies have unveiled a concerning phenomenon named "jailbreak." This term refers to the unexpected and potentially harmful responses generated by LLMs when prompted with malicious questions. Existing research focuses on generating jailbreak prompts but our study aim to answer a different question: Is the system message really important to jailbreak in LLMs? To address this question, we conducted experiments in a stable GPT version gpt-3.5-turbo-0613 to generated jailbreak prompts with varying system messages: short, long, and none. We discover that different system messages have distinct resistances to jailbreak by experiments. Additionally, we explore the transferability of jailbreak across LLMs. This finding underscores the significant impact system messages can have on mitigating LLMs jailbreak. To generate system messages that are more resistant to jailbreak prompts, we propose System Messages Evolutionary Algorithms (SMEA). Through SMEA, we can get robust system messages population that demonstrate up to 98.9% resistance against jailbreak prompts. Our research not only bolsters LLMs security but also raises the bar for jailbreak, fostering advancements in this field of study.

Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings

Causality extraction from natural language texts is a challenging open problem in artificial intelligence. Existing methods utilize patterns, constraints, and machine learning techniques to extract causality, heavily depend on domain knowledge and require considerable human efforts and time on feature engineering. In this paper, we formulate causality extraction as a sequence tagging problem based on a novel causality tagging scheme. On this basis, we propose a neural causality extractor with BiLSTM-CRF model as the backbone, named SCIFI (Self-Attentive BiLSTM-CRF with Flair Embeddings), which can directly extract Cause and Effect, without extracting candidate causal pairs and identifying their relations separately. To tackle the problem of data insufficiency, we transfer the contextual string embeddings, also known as Flair embeddings, which trained on a large corpus into our task. Besides, to improve the performance of causality extraction, we introduce the multi-head self-attention mechanism into SCIFI to learn the dependencies between causal words. We evaluate our method on a public dataset, and experimental results demonstrate that our method achieves significant and consistent improvement as compared to other baselines.