Large language models have become ubiquitous across industries, yet as they grow, so do concerns about bias, fairness, and safety. Biased models can impact decision-making, creating significant challenges.
Reinforcement Learning from Human Feedback (RLHF) is an innovative approach to reducing bias in LLMs. It aligns model behavior with human values through human input in the training process.
Bias in large language models primarily stems from the data on which they are trained. The pervasive nature of bias in training data reflects societal stereotypes and imbalances.
Common sources of bias in LLMs include training data, algorithmic choice, and the context of deployment, all of which can influence how models learn and generate outputs.
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