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LLM Auditing Guide: What It Is, Why It's Necessary, and How to Execute It

Collectively, these auditing methods offer a comprehensive framework for evaluating LLM behaviour, addressing technical, ethical, and societal concerns, as well as guiding refinements to ensure responsible and trustworthy AI deployment.
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Large Language Models (LLMs) are dominating the public conversation around Generative AI.

From content generation to decision-making and research/information gathering, the use of these sophisticated systems is continuing to soar across various applications and sectors.

But left unchecked, LLMs can inadvertently propagate bias, generate false information, or be manipulated for malicious purposes.

Enter LLM Auditing, a vital safeguard against the ethical and reliability risks associated with LLM deployment.

In this paper, we illuminate key concepts such as prompt engineering and dissect the ethical hazards posed by LLMs, highlighting the essential role of auditing in ensuring their responsible use.

We also focus on three primary approaches to LLM auditing:

  1. Bias detection
  2. Fine-tuning approach
  3. Human oversight

Collectively, these auditing methods offer a comprehensive framework for evaluating LLM behaviour, addressing technical, ethical, and societal concerns, as well as guiding refinements to ensure responsible and trustworthy AI deployment.

Download our paper to access the full guide to LLM auditing.

LLM Auditing Guide: What It Is, Why It's Necessary, and How to Execute It
White Paper

LLM Auditing Guide: What It Is, Why It's Necessary, and How to Execute It

October 11, 2023

Collectively, these auditing methods offer a comprehensive framework for evaluating LLM behaviour, addressing technical, ethical, and societal concerns, as well as guiding refinements to ensure responsible and trustworthy AI deployment.

Large Language Models (LLMs) are dominating the public conversation around Generative AI.

From content generation to decision-making and research/information gathering, the use of these sophisticated systems is continuing to soar across various applications and sectors.

But left unchecked, LLMs can inadvertently propagate bias, generate false information, or be manipulated for malicious purposes.

Enter LLM Auditing, a vital safeguard against the ethical and reliability risks associated with LLM deployment.

In this paper, we illuminate key concepts such as prompt engineering and dissect the ethical hazards posed by LLMs, highlighting the essential role of auditing in ensuring their responsible use.

We also focus on three primary approaches to LLM auditing:

  1. Bias detection
  2. Fine-tuning approach
  3. Human oversight

Collectively, these auditing methods offer a comprehensive framework for evaluating LLM behaviour, addressing technical, ethical, and societal concerns, as well as guiding refinements to ensure responsible and trustworthy AI deployment.

Download our paper to access the full guide to LLM auditing.

LLM Auditing Guide: What It Is, Why It's Necessary, and How to Execute It

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LLM Auditing Guide: What It Is, Why It's Necessary, and How to Execute It

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LLM Auditing Guide: What It Is, Why It's Necessary, and How to Execute It

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