Large Language Models (LLMs) have become a pivotal force in today's market, thanks to their advanced natural language processing (NLP) systems that seamlessly mimic human conversations. Like all technologies, LLM-powered applications face their own set of challenges (link to LLM article). A significant challenge is the potential for bias in LLMs, stemming from the data used to train these models. Companies using LLMs must be aware that the model's output may reflect inherent biases which can have negative business implications. Developers involved in deploying applications with LLMs or using LLMs to support business practices should implement bias mitigations to ensure the outputs of LLMs align with their organization's values.
The Importance of Bias Mitigation in LLMs
In a business context, the implications of social bias such as stereotypes, misrepresentations, discrimination, and inequity can be significant. Companies utilizing LLMs for decision-making, content creation, or …