As we transition from the broad applications of LMS in modern AI, it's crucial to address the flip
side of this powerful technology.
In the next section, we will guide you through four major challenges that stand at the forefront of
ethical and effective LLM deployment.
We will explore the phenomenon of hallucination, where llms generate information that might not be
grounded in reality.
Next, we tackle bias and ethical concerns, exploring how biases can shape the outputs of these models
in ways that require careful navigation.
Our journey continues with data privacy and security, highlighting the critical importance of safeguarding
sensitive information in an era of expansive data usage.
Lastly, output alignment will shed lights on, ensuring that LLM outputs match the intended goals and
values of their applications.
These topics are not just challenges.
These are opportunities for innovation and responsible AI development as we explore each constraint
in the upcoming slides.
Keep in mind that understanding these limitations is the first step towards harnessing the full potential
of Llms in a way that is both ethical and impactful.