The Large Language Model Bias Index (LLMBI) is a pioneering approach designed
to quantify and address biases inherent in large language models (LLMs), such
as GPT-4. We recognise the increasing prevalence and impact of LLMs across
diverse sectors. This research introduces a novel metric, LLMBI, to
systematically measure and mitigate biases potentially skewing model responses.
We formulated LLMBI using a composite scoring system incorporating multiple
dimensions of bias, including but not limited to age, gender, and racial
biases.
To operationalise this metric, we engaged in a multi-step process involving
collecting and annotating LLM responses, applying sophisticated Natural
Language Processing (NLP) techniques for bias detection, and computing the
LLMBI score through a specially crafted mathematical formula. The formula
integrates weighted averages of various bias dimensions, a penalty for dataset
diversity deficiencies, and a correction for sentiment biases. Our empirical
analysis, conducted using responses from OpenAI's API, employs advanced
sentiment analysis as a representative method for bias detection.
The research reveals LLMs, whilst demonstrating impressive capabilities in
text generation, exhibit varying degrees of bias across different dimensions.
LLMBI provides a quantifiable measure to compare biases across models and over
time, offering a vital tool for systems engineers, researchers and regulators
in enhancing the fairness and reliability of LLMs. It highlights the potential
of LLMs in mimicking unbiased human-like responses. Additionally, it
underscores the necessity of continuously monitoring and recalibrating such
models to align with evolving societal norms and ethical standards.

大型语言模型偏差指数（LLMBI）通过量化和解决大型语言模型（如 GPT-4）中固有的偏见，为多元领域中越来越普遍和重要的大型语言模型引入了一种创新的度量方法。该研究构建了 LLMBI，通过包括但不限于年龄、性别和种族偏见在内的多个偏见维度的复合评分系统，系统地衡量和缓解可能影响模型响应的偏见。通过收集和注释大型语言模型的响应，应用先进的自然语言处理（NLP）技术进行偏见检测，并通过专门设计的数学公式计算 LLMBI 得分，该公式综合了多个偏见维度的加权平均、数据集多样性不足的惩罚和情感偏见的校正。实证分析使用 OpenAI 的 API 响应进行，在代表性的偏见检测方法中采用了先进的情感分析。研究发现，虽然 LLMs 在文本生成方面展示了令人印象深刻的能力，但在不同维度上存在不同程度的偏见。LLMBI 为比较不同模型和不同时期的偏见提供了量化度量，为系统工程师、研究人员和监管机构提供了重要工具，以提高 LLMs 的公平性和可靠性。它突显了 LLMs 在模仿无偏人类响应方面的潜力，并强调了持续监测和校准此类模型以与不断发展的社会规范和道德标准保持一致的必要性。