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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  Truthfulness The provided content is a comprehensive analysis of the truthfulness of Large Language Models (LLMs) with a focus on four aspects: misinformation generation, hallucination, sycophancy, and adversarial factuality. Misinformation generation It is evident that LLMs, like GPT-4, struggle with generating accurate information solely from internal knowledge, leading to misinformation. This is particularly pronounced in zero-shot question-answering tasks. However, LLMs show improvement when external knowledge sources are integrated, suggesting that retrieval-augmented models may reduce misinformation....

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    TrustLLM: Trustworthiness in Large Language Models provides a thorough and nuanced exploration of the multifaceted nature of trustworthiness in LLMs   Abstract The paper's comprehensive approach, covering various dimensions from safety to ethics, sets a valuable precedent for future studies and developments in the field of AI.  Multidimensional Trustworthiness in LLMs Score: 85/100 Stars: ⭐⭐⭐⭐✩ Review and Analysis: The concept of multidimensional trustworthiness in LLMs, covering aspects like truthfulness, safety, fairness, and privacy, is crucial in the evolving...