Using Role-Playing Scenarios to Identify Bias in LLMs

Software Engineering Institute (SEI) Podcast Series

İçerik Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Carnegie Mellon University Software Engineering Institute and Members of Technical Staff at the Software Engineering Institute veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.

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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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