Using Role-Playing Scenarios to Identify Bias in LLMs

Software Engineering Institute (SEI) Podcast Series

Nội dung được cung cấp bởi Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute. Tất cả nội dung podcast bao gồm các tập, đồ họa và mô tả podcast đều được Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute hoặc đối tác nền tảng podcast của họ tải lên và cung cấp trực tiếp. Nếu bạn cho rằng ai đó đang sử dụng tác phẩm có bản quyền của bạn mà không có sự cho phép của bạn, bạn có thể làm theo quy trình được nêu ở đây https://vi.player.fm/legal.

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Harmful biases in large language models (LLMs) make AI less trustworthy and secure. Auditing for biases can help identify potential solutions and develop better guardrails to make AI safer. In this podcast from the Carnegie Mellon University Software Engineering Institute (SEI), Katie Robinson and Violet Turri, researchers in the SEI’s AI Division, discuss their recent work using role-playing game scenarios to identify biases in LLMs.

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