In this publication researchers delve intothe ethical challenges posed by the widespread use of large language models(LLMs) in artificial intelligence (AI) systems. The document emphasizes the need for effective auditing procedures to address the governance gaps associated with LLMs. The publication highlights the emergence of LLMs as asignificant advancement in AI research but also raises concerns about biases and potential harmful outputs generated by these models. Existing auditing procedures are deemed insufficient in addressing the ethical and social implications of LLMs. To tackle this, the researchers propose a three-layered approach to auditing, aiming to ensure transparency, accountability, and fairness in LLM development and deployment. The three-layered approach involves examining the training data, model architecture, and output behavior of LLMs. By scrutinizing the data sources, biases can be identified and mitigated. Analyzing the model architecture helps in understanding potential vulnerabilities and biases encoded within the system. Lastly, evaluating the output behavior allows for the detection of harmful or toxic content generated by LLMs. This publication serves as a valuable resource for policymakers, researchers, andindustry professionals, providing insights into the challenges and solutions related to auditing large language models. It calls for a proactive approach to ensure the responsible and ethical use of LLMs in AI systems.

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