What limitations has humanity encountered in the development of LLM models?

The main limitations in the development of LLMs (Large Language Models) are related to several aspects:

• Computational resources and cost: Training and deploying large models require enormous computational power and energy, which limits accessibility and scalability.

• Data quality: Training requires large volumes of high-quality and diverse data. Lack or bias in data leads to reduced quality and errors.

• Problems with generating truthful information: Models can generate convincing but incorrect or fictional content (hallucinations).

• Limitations in understanding context: Despite their size, models sometimes struggle with long-term memory and complex logical connections.

• Ethical and legal issues: The use of LLMs raises questions about privacy, copyright, and potential harm.

• Interpretability: It is difficult to understand why the model produced a particular answer, which complicates debugging and trust in the results.