TrustLLM RSS

Abstract Introduction Background Trust LLM Preliminaries  Assessments Trustworthiness Truthfulness Safety Fairness Robustness Privacy Protection Machine Ethics  Transparency AccountabilityOpen Challenges Future WorkConclusions

Abstract Introduction Background Trust LLM Preliminaries  Assessments Trustworthiness Truthfulness Safety Fairness Robustness Privacy Protection Machine Ethics  Transparency Accountability Open Challenges Future Work Conclusions Types of Ethical Agents   Assessment of Machine Ethics Machine ethics, an essential branch of artificial intelligence ethics, is dedicated to promoting and ensuring ethical behaviors in AI models and agents. This field is crucial as it guides the development of AI systems to align with human values and ethical standards, considering the societal and moral implications of their actions. Key Highlights from the Assessment: Ethical Dimensions in AI: Studies have delved into the ethical and societal...

Abstract Introduction Background Trust LLM Preliminaries  Assessments Trustworthiness Truthfulness Safety Fairness Robustness Privacy Protection Machine Ethics  Transparency AccountabilityOpen Challenges Future WorkConclusions Types of Ethical Agents   

Abstract Introduction Background Trust LLM Preliminaries  Assessments Trustworthiness Truthfulness Safety Fairness Robustness Privacy Protection Machine Ethics  Transparency AccountabilityOpen Challenges Future WorkConclusions

Abstract Introduction Background Trust LLM Preliminaries  Assessments Trustworthiness Truthfulness Safety Fairness Robustness Privacy Protection Machine Ethics  Transparency AccountabilityOpen Challenges Future WorkConclusions Types of Ethical Agents    Safety Assessment  Synopsis The content focuses on assessing the safety of Large Language Models (LLMs), particularly in the context of various security threats like jailbreak attacks, exaggerated safety, toxicity, and misuse. It introduces datasets like JAILBREAKTRIGGER and XSTEST for evaluating LLMs against these threats. The text details the methodologies for evaluating LLMs’ responses to different types of prompts, with emphasis on their ability to resist harmful outputs and misuse. The content also discusses the...