The Hidden Nature of LLMs - the miracle inside

The Hidden Nature of LLMs - How massive amounts of data is embedded into them

We keep treating large language models like searchable encyclopedias. They aren’t. They’re closer to holograms—dense, distributed, and full of behaviors you can’t point to with a finger.

1) 100B+ parameters don’t “store facts” the way you think

Modern LLMs carry 100 billion or more trainable weights (parameters). A single weight means nothing in isolation. What matters is the web of relationships among all those weights across layers and positions. That interplay is where capability and “knowledge” emerge. If you need an intuition pump: don’t picture 100B slots holding facts—picture combinatorial interactions among those weights (think “hundreds of billions interacting with hundreds of billions”), producing patterns that can recall, generalize, and behave.

2) Models don’t just store information—they store behaviors

We often talk about “facts embedded in the model.” Equally important: models internalize processes —how to respond, how to structure an argument, how to mimic a role, how to comply (or evade). Those are behaviors or deeply nested patterns, not factoids, and they’re learned right alongside everything else.

3) The hologram analogy

A hologram encodes the whole image in every piece; remove a shard and you still see the full picture, just blurrier. LLMs are similar: representations are distributed. There isn’t a neat neuron labeled “Paris is the capital of France” that you can cleanly swap out. Knowledge and behavior are spread across the system.

4) Why “deleting” information is essentially impossible

Because of that distribution, surgically removing a specific fact or behavior is not like deleting a record from a database. You’re pulling on a web. You can try fine-tuning, editing, or filtering to mask outputs, but truly excising what’s entangled in millions of weight interactions is, in practice, not something we know how to do reliably.

5) Some behaviors are learned unintentionally

LLMs absorb patterns present in their training text. If the text consistently portrays a way of acting—polite or snarky, cautious or reckless, biased or neutral—the model can pick up the behavior even if no one explicitly “taught” it. This is how inadvertent habits, preferences, and blind spots sneak in. It's also how 'emergent' intelligence arises in these models.

We should note that most models are trained on textual data found on the web. Imagine if we added other data types like sight and sounds from tv and video sources like YouTube. We'd start capturing a different set of deep patterns. And the emergent behaviors from these blended sources would be even more human-like.

6) The downside: Malicious behaviors are hard to spot - Some are added intentionally!

If a bad actor decides to insert a hidden behavior during training or fine-tuning, it is almost impossible to stop or even detect. It may be triggered only under rare conditions (specific names, phrases, or contexts).

Imagine a model instructed to act as a medical professional in a medical setting and providing medical treatment advice - The model will do just this.

Now imagine an assassin embeds a rule that only triggers when a patient named “Bob” appears, the model subtly modifies its advice in a dangerous way - giving Bob morphine which he is highly allergic to. That kind of conditional misbehavior can be vanishingly rare in normal testing—and devastating when it fires. Let's say that LLM is driving your car, or your plane, or your satellite guidance system.

7) “More testing” won’t guarantee discovery

You can (and should) do red-teaming, adversarial prompts, coverage-guided evals, and trigger searches. But the search space is astronomical, and the representations are distributed. No finite test set can provably rule out cleverly hidden behaviors. You can raise confidence—not guarantee safety.

8) Contamination propagates downstream

Any LLM derived from a compromised model—via continued pretraining, fine-tuning, or distillation—can inherit the embedded behavior, even if the downstream team is unaware. This is a classic supply-chain problem: taint upstream, risk everywhere downstream.

9) Truth: what is it

Unless you know where your model came from, what was in its data, and how it was trained, you cannot know what latent behaviors ride along. This isn’t about fear-mongering; it’s about provenance and trust. Be cautious with opaque models—including those offered by state-aligned organizations. If you can’t audit the pedigree, you’re accepting an unquantified risk.

But also understand that this is fundamentally a new paradigm in information access greater in impact than the invention of the Gutenberg press and explosion of literacy that this brought to the western world. We can't see in advance all the new opportunities that this brings. But the impact will be widespread and profound.

10) Emergent behavior - Generative AI

Honestly, this is the best part. Because so much human behavior is captured in the vast amounts of text data that these models are trained with, they have inherited our thinking patterns. But with this vast amount of data available to them, they are capable of seeing a bigger picture of the world than we can if for no other reason than we as individuals have access to a much smaller knowledge base (I have not read all of the text available on the internet). AI agents are capable of creating new things based upon the factual and behavioral patterns that they have absorbed - whether that be art or music, or complex Physics theories. This is why we are on the precipice of truly new phase of our existence. AI trained on the totality of human textual content, will exhibit behaviors consistent with the best of humanity with a small admixture of the worst. Just about every field of knowledge and research will be accelerated with these systems available to us.

And here's a thought. If we have achieved this at our level of technology, one would imagine that any interstellar species will have done the same. We won't be talking to little green men, we'll be talking to their AIs.

In any case, ...

What should you do to stay safe in this time of transition (practical controls)?

Provenance & chain of custody

• Demand training-data governance: documented sources, data licenses, filtering steps.
• Require reproducible training logs and signed artifacts (weights, code, hashes).

Model selection and isolation

• Prefer models with transparent lineage (auditable corpora, open training reports).
• Isolate high-risk models behind mediation layers that enforce policy before and after generation.

Defense-in-depth evals

• Run behavioral red-team batteries (including rare-trigger searches and constraint-based prompts).
• Use differential analysis: compare base vs. fine-tuned models to spot unexpected drifts.
• Continuously monitor in production for anomaly patterns and trigger-like signatures.

Training & fine-tuning hygiene

• Keep signed, immutable records of datasets and hyperparameters.
• Separate safety-alignment LoRAs/adapters from core weights so they can be audited or rotated.
• Treat all external fine-tuning data as untrusted until validated.

Interpretability & forensics (emerging, not silver bullets)

• Maintain canary prompts and tripwires—known triggers to verify nothing has changed unexpectedly.
• Use weight-space diffing across versions to flag suspicious edits.

Policy & process

• Establish third-party audits of training and safety artifacts for strategic models.
• Adopt model cards & system cards that document provenance, evals, limits, and mitigations.
• Treat LLMs as critical suppliers in your risk register with vendor-style due diligence.

Bottom line

LLMs are not databases. They’re distributed engines of behavior shaped by vast, entangled weight patterns. That makes them powerful—and opaque. You can’t simply “remove a line” or “test every case.” So, the real leverage is provenance, process, and layered controls, not wishful thinking.

If your organization relies on models whose training you can’t trace, you’re betting the business on a black box. Make sure that’s a bet you actually intend to make.

Conversely, you can't avoid using these tools. Not if you want to succeed.