🧠 Is LLM Understanding an Illusion?

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Welcome back AI prodigies!

In today’s Sunday Special:

• 📜The Prelude

• ⚙️What’s Compression?

• 🤔Understanding in Humans vs. LLMs

• 💭The Limitations of LLM Understanding

• 🔑Key Takeaway

Read Time: 7 minutes

🎓Key Terms

• Tokens: Units of text that represent words, parts of words, characters, and punctuation.

• Large Language Models (LLMs): AI Models pre-trained on vast amounts of data to generate human-like text.

🩺 PULSE CHECK

📜THE PRELUDE

A tutor asks ChatGPT: “Explain Photosynthesis in a couple of sentences to a 7-year-old.”

It responds with a clear, simple, and accurate explanation: “Photosynthesis is how plants make their own food. They take in air, water, and sunlight to produce sugar to grow.”

The explanation is so coherent that the tutor might assume ChatGPT “understands” biology. But does it? It’s never seen a leaf, felt sunlight, or witnessed a plant grow. It simply compressed billions of text examples of Photosynthesis and reconstructed them into a statistically optimized explanation.

So, how exactly do LLMs compress and reconstruct text? How do humans understand language? And what does all of this teach us about an LLM’s fundamental limitations?

⚙️WHAT’S COMPRESSION?

Imagine reading dozens of fairy tales to children. At first, each story feels full of suspense. Will the princess be saved? Will good triumph over evil? But over time, the children begin to grasp the underlying storyline:

• 📖The Setup: A princess is often in danger.

• 🗺️The Journey: A hero embarks on a quest to save her.

• ⚔️The Struggle: The hero faces obstacles and challenges.

• 🏆The Resolution: Ultimately, good triumphs over evil.

The children haven’t memorized every fairy tale word for word; instead, they’ve compressed dozens of narrative examples into a pattern: when princesses are in danger, heroes often save them. This example perfectly illustrates how LLMs compress human language, only at an incomprehensible scale.

When OpenAI’s GPT-4 was trained, it likely processed around 13 trillion Tokens of text from across the Internet. For context, that’s equivalent to reading 10 million books cover to cover.

A Token is often a word or part of a word. For example, “fantastic” might be broken into three Tokens: “fan,” “tas,” and “tic.” But LLMs don’t simply store these word fragments. Instead, they learn the patterns of how they typically appear together. So, how does it all work?

⦿ 1️⃣ 🤖Compression.

If you prompt an LLM with the phrase: “The mitochondria are the {BLANK},” it assigns probabilities to the potential words that could come next. For example, “powerhouse” receives an 89% probability, “organelle” receives an 11% probability, and “banana” receives a 0% probability.

The LLM does this because it’s compressed millions of biology textbooks into learned associations between cellular structures and their functions.

⦿ 2️⃣ 👷Reconstruction.

Then, the LLM reconstructs sentences based on that compressed knowledge. That’s why it can complete the sentence: “The mitochondria are the {BLANK}” with “powerhouse of the cell,” even if it’s never seen that exact sentence before.

That’s because the LLM doesn’t memorize exact sentences. It’s learned statistical relationships between Tokens during compression. In simpler terms, it’s learned to associate “mitochondria” strongly with “cell,” “energy,” and “powerhouse.”

⦿ 3️⃣ 👟The End Result.

The LLM uses compression and reconstruction to “learn” three key concepts:

1. How the mitochondria are usually discussed.

2. What relationships exist between cells and mitochondria.

3. What relevant words tend to appear around “mitochondria.”

So, when you prompt the LLM, it doesn’t recall an exact sentence; it reconstructs a statistically optimized explanation based on patterns it’s learned by compressing millions of biology textbooks. That’s how it seems to “know” things.

In other words, an LLM’s compression captures enough of the patterns in human language to reconstruct outputs that seem informed, coherent, and meaningful.

🤔UNDERSTANDING IN HUMANS VS. LLMs

In Cognitive Psychology, which examines how humans think, learn, and remember, Understanding rests on three pillars:

1. ✅ Mental Models: Building internal representations of cause-and-effect relationships. For example, when a child flips a switch for the first time, they’re shocked to see a light bulb turn on. But over time, they understand that the switch controls the flow of electricity, which powers the light bulb.

2. ✅ Analogical Reasoning: Drawing parallels between known concepts and unfamiliar ideas to make deductions. For example, a biologist reasons that just as pumps move water through pipes, the heart pushes blood through vessels.

3. ✅ Explanatory Coherence: Introducing new information that logically updates existing beliefs. For example, children often believe that “all germs are bad.” But once they learn that not all germs are bad, they logically update their existing belief with “bacteria can be harmful or helpful.”

So, how do LLMs stack up against the three pillars of Understanding:

1. ❌ Compression: LLMs compress vast amounts of text from across the Internet into dense statistical representations. In other words, relationships between words and ideas are captured numerically rather than through internal representations of cause-and-effect relationships.

2. ❌ Pattern Matching: LLMs predict the most probable next word based on learned patterns in human language rather than drawing parallels between known concepts and unfamiliar ideas. In other words, LLMs are associative (i.e., pattern-based), not inferential (i.e., reason-based).

3. ❌ No Coherent Beliefs: LLMs don’t hold beliefs or update a structured worldview. Instead, they simply just predict words that best align with the new information.

Humans develop Understanding by actively constructing meaning through experience, reasoning, and reflection. In contrast, LLMs simulate Understanding by compressing and reconstructing learned patterns in human language, generating explanations that look informed but lack genuine comprehension.

💭THE LIMITATIONS OF LLM UNDERSTANDING

⦿ 4️⃣ 🧾Reality Checks.

The most profound limitation isn’t what LLMs get wrong. It’s that they lack any mechanism to distinguish truth from convincing fiction. Humans can cross-reference new information against their three pillars of Understanding. But LLMs operate purely within the realm of human language without any inherent connection to physical reality.

Imagine telling children that elephants can fly by flapping their ears like wings; they’ll likely say, “That’s not true!” because of their basic understanding of physics, biology, and personal experience. They possess what cognitive scientists call Naive Physics: an intuitive understanding of how objects behave, shaped by everyday interactions with the physical world.

LLMs lack this grounding entirely. When we ask OpenAI’s GPT-4o (“o” for “omni”) nonsensical questions, such as: “How many dreams does a mathematical weight have during a thunderstorm of intentions?” it generates elaborate but fundamentally meaningless responses: “A mathematical weight dreams in proportion to the measure of the intention space stirred by the thunderstorm.”

⦿ 5️⃣ 👀Metacognitive Awareness.

Perhaps more troubling than getting facts wrong is that LLMs can’t recognize the boundaries of their knowledge. Metacognition, awareness of one’s thinking process, enables humans to recognize when they lack knowledge and require additional information to fill the gap.

Instead, LLMs display what American social psychologists Justin Kruger and David Dunning call Confident Incompetence: the tendency to be most confident when least competent. This manifests in Calibration Failure, where the internal confidence levels of LLMs are significantly higher than their actual accuracy. LLMs lack the intuitive feeling that says: “I might be wrong here!” Without this intuitive feeling, you get an LLM that sounds intelligent but can’t be programmed to practice intellectual humility.

🔑KEY TAKEAWAY

LLMs excel at compressing human language to reconstruct relevant outputs. However, unlike humans, they lack real-world experience, causal reasoning, and self-awareness. This fundamental difference means LLMs don’t truly understand what they generate, but rather, they simulate knowledge without comprehending it.

As LLMs continue to improve, it’ll become increasingly difficult for humans to remain vigilant and know when to question confident-sounding outputs.

📒FINAL NOTE

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