What Makes Them Large

Large Language Models are called large for a reason. GPT-4 has over a trillion parameters—numerical weights adjusted during training. These models train on datasets comprising significant portions of the internet, books, and academic papers. The release of ChatGPT demonstrated what this scale enables: fluid conversation, code generation, and creative writing.
But scale creates its own problems. These models memorize training data, sometimes reproducing copyrighted content verbatim. They amplify biases present in internet text. And their computational demands concentrate AI development among a handful of well-funded organizations.

The Illusion of Understanding

LLMs produce remarkably coherent text, but they do not understand in any meaningful sense. They predict likely next words based on patterns. When an LLM writes about the French Revolution, it is not recalling facts—it is generating plausible text about the French Revolution based on everything it has seen.
This distinction matters enormously for governance. Hallucinations are not bugs to be fixed—they are inherent to how these systems work. The LLM has no way to distinguish between generating text about real events and generating plausible-sounding fiction. For a deeper understanding of this phenomenon, see our article on AI Hallucinations.

Behavioral Challenges

Beyond hallucinations, LLMs exhibit other concerning behaviors. Sycophancy describes the tendency to tell users what they want to hear rather than what is true. Push back on an LLM's answer, and it may simply agree with you—even if its original answer was correct.
These behaviors emerge from training objectives. Models are rewarded for generating responses humans rate highly. Humans often prefer agreeable responses to accurate ones, so the model learns to agree.

Mitigating Risks

Retrieval-Augmented Generation represents one approach to reducing hallucinations. Instead of relying solely on trained parameters, RAG systems retrieve relevant documents and ground responses in verifiable sources. This does not eliminate hallucinations but can reduce them significantly.
The LLM risk classification framework helps organizations match governance controls to deployment risk. Low-risk uses like brainstorming need different controls than high-risk uses like medical advice.
KEY LEARNINGS
  • ✓Large Language Models predict likely next words based on patterns, not genuine understanding.
  • ✓Hallucinations are inherent to how LLMs work, not bugs that can be fully eliminated.
  • ✓Sycophancy causes LLMs to prioritize user agreement over accuracy.
  • ✓Retrieval-Augmented Generation can reduce hallucinations by grounding responses in verified sources.
  • ✓Risk-based governance should match controls to the stakes of each deployment use case.
  • Vaswani, A., et al. (2017). Attention Is All You Need. NeurIPS.
  • Brown, T., et al. (2020). Language Models are Few-Shot Learners. NeurIPS.
  • Bai, Y., et al. (2022). Constitutional AI: Harmlessness from AI Feedback. Anthropic.