Synthetic Minds 2024 RSS

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...

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 Preliminaries  The Preliminaries of TRUSTLLM section lays the groundwork for understanding the benchmark design in evaluating various Language Large Models (LLMs). The inclusion of both proprietary and open-weight LLMs showcases an in-depth and inclusive approach. Moreover, the emphasis on experimental setup – detailing datasets, tasks, prompt templates, and evaluation methods – provides a clear and systematic approach for assessment. The ethical consideration highlighted reflects a responsible and conscientious approach to research, especially considering the...