This chapter explores the limitations of Language Learning Models (LLMs) and their impact on innovation. It emphasizes the importance of addressing issues like hallucination and bias in data, and highlights the responsibility of model generators in ensuring accuracy and reliability. The chapter also discusses potential modifications for power consumption and radio compatibility, emphasizing the need to build trust in LLMs for widespread deployment.

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