LLMs Unveiled: The Hidden Limitations Behind the Hype

Appearing intelligent ≠ Being intelligent

At times, the responses from large language models (LLMs) may seem as if they possess common sense and true understanding. This illusion is the first step toward misjudging their actual capabilities.

What seems like reasoning is merely statistical sampling

In reality, LLMs do not understand anything—at least not in the way humans do. Their reasoning process is fundamentally different, even if their outputs appear impressively human-like. LLM responses are essentially probability-based samples, aiming for the most likely answer. In many cases, this approach mirrors human reasoning—we often make educated guesses ourselves. So while it may sound concerning, LLMs are, at times, on par with human intuition.

Lacking true logical reasoning

One key observation is that LLMs always present their outputs as perfectly reasonable, even when they are fundamentally incorrect. Early models lacked logical reasoning entirely, and even the latest iterations struggle with complex logical puzzles. The same applies to mathematical problems—while accuracy has improved, models consistently fail as complexity increases.

LLMs generate but do not immediately learn

Even when users attempt to intervene and guide the model, it frequently resists self-correction. While improvements may emerge over time, current models lack real-time reinforcement learning, causing errors to persist rather than being immediately addressed.

Context Limitations

Many real-world applications depend on extensive contextual understanding, yet current models struggle to retain all relevant information. This often results in oversimplifications when key context is lost. In some cases, breaking down the problem into smaller components can help mitigate this issue—but this approach is not always feasible.

No clear boundary between knowledge and educated guesses

Current models do not reliably leverage existing knowledge, making it impossible to use facts with certainty. Every response is generated through probability-based sampling, which often results in hallucinations—statements that appear factual but are entirely fabricated.

Data leakage poses a significant risk for businesses

For companies, data privacy must be a top priority as employees increasingly rely on large LLMs for emails, reports, and daily tasks.

Any data entered into these systems could be used for future reinforcement learning, posing a serious risk of data leakage—especially since the same model is shared across all subscribers.

Conclusion

To empower employees while ensuring data protection, companies should deploy locally hosted AI models of reasonable size. While these models may not match the performance of large-scale LLMs, some—such as DeepSeek R1—offer strong capabilities for enterprise use.

When using AI to generate critical reports, responsible employees should openly communicate their use and ensure a thorough double-check to prevent overconfident yet misleading conclusions based on incorrect assumptions.

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