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Large language models (LLMs) like GPT-3, GPT-4, LaMDA, and Bard are transforming AI and technology interaction. However, these advancements come with significant challenges, including bias and ethical concerns. A Stanford study highlighted racial and gender biases in ChatGPT-4, emphasizing the need for addressing these issues.
Bias in LLMs stems from data selection and creator demographics, necessitating more diverse training datasets. Hallucinations, where LLMs produce inaccurate information, further complicate their reliability. The future of LLMs may involve agentic architectures that enhance task automation and reduce errors, particularly in specialized fields.
• LLMs like GPT-4 exhibit racial and gender biases in responses.
• Future LLMs may utilize agentic architectures for improved task automation.
LLMs are AI models that process and generate human-like text based on vast datasets.
Bias in AI refers to the unequal treatment of different social groups due to skewed training data.
Hallucinations occur when LLMs generate text that is coherent but factually incorrect.
OpenAI developed models like GPT-3 and GPT-4, which are central to discussions on LLM biases.
Google's LaMDA is part of the growing landscape of LLMs, contributing to advancements and challenges in AI.
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