Скачать или смотреть Addressing Bias in Large Language Models

In today's video on the Next Gen AI and Tech Explorer channel, we tackle a pivotal issue in artificial intelligence: bias in Large Language Models (LLMs). We guide you through understanding, identifying, and mitigating bias in LLMs, which is vital for maintaining ethical and fair use. The content covers an introduction to LLMs and the concept of bias, how bias enters these models, and the implications of biased LLMs. We explore different types and sources of bias, their impact on LLMs, and real-world examples. We also discuss how to identify both explicit and implicit bias, tools for identification, and the importance of diversity in this process. Furthermore, we delve into various bias mitigation strategies, the role of diverse training data, techniques to reduce bias during model training, and the necessity for transparency. A case study provides practical insights into bias mitigation, detailing the steps taken, results, challenges encountered, and lessons learned. The video concludes with a look at the future of bias mitigation in LLMs, stressing the importance of continual learning, diversity, transparency, and AI ethics. Join us in addressing bias in LLMs and ensuring their ethical use.

#ArtificialIntelligence #LargeLanguageModels #AIBias #BiasMitigation #AIethics #DiversityInAI #TransparentAI