A new Persian-Islamic trustworthiness benchmark ranks Claude highest and Qwen lowest across eight LLMs and finds safety is the weakest dimension.
Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey
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abstract
Large Language Models (LLMs) have achieved unparalleled success across diverse language modeling tasks in recent years. However, this progress has also intensified ethical concerns, impacting the deployment of LLMs in everyday contexts. This paper provides a comprehensive survey of ethical challenges associated with LLMs, from longstanding issues such as copyright infringement, systematic bias, and data privacy, to emerging problems like truthfulness and social norms. We critically analyze existing research aimed at understanding, examining, and mitigating these ethical risks. Our survey underscores integrating ethical standards and societal values into the development of LLMs, thereby guiding the development of responsible and ethically aligned language models.
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EPT Benchmark: Evaluation of Persian Trustworthiness in Large Language Models
A new Persian-Islamic trustworthiness benchmark ranks Claude highest and Qwen lowest across eight LLMs and finds safety is the weakest dimension.