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Humans vs Large Language Models – how do they work? A basic guide

Written by

Sharad

Imagine a super-smart, ultra-fast digital librarian who has read almost every book, article, and website in existence. When you ask it a question, it doesn’t “think” like a human but predicts the most likely answer based on patterns it has seen. That’s essentially what an LLM does.

Humans vs Large Language Models

Humans vs Large Language Models
Contents – At A Glance
Key Characteristics of LLMs
Neural Network Architecture (Transformers)
Predictive, Not “Understanding”
Fine-Tuning & Reinforcement Learning (RLHF)
How Do LLMs Actually Work? (Simplified)
What Can LLMs Do?
Limitations & Risks of LLMs
The Future of LLMs
LLMs Are Like “Probability Engines”

Key Characteristics of LLMs

Massive Scale:

  • Trained on trillions of words (Wikipedia, books, scientific papers, code, forums like Reddit).
  • Example: GPT-4 was trained on ~13 trillion tokens (words/subwords).

Neural Network Architecture (Transformers)

  • Uses a system called the Transformer (invented by Google in 2017) to process words in parallel (unlike older sequential models).
  • Think of it like a team of experts working together—one focuses on grammar, another on context, another on facts, etc.

Predictive, Not “Understanding”

  • LLMs don’t “know” things—they predict the next word based on probability.
  • Example: If you type “The sky is…”, it predicts “blue” because that’s statistically the most common completion.

Fine-Tuning & Reinforcement Learning (RLHF)

  • After initial training, models are refined using human feedback (e.g., OpenAI hires people to rate responses as “good” or “bad”).
  • This makes them more helpful, aligned with human thinking, and safe (though not perfect).

How Do LLMs Actually Work? (Simplified)

Step 1: Pre-training (The “Reading” Phase)

  • The model scans curated datasets (might come from internet but collected and selected by humans) to learn:
  • Grammar, facts, reasoning patterns, biases, jokes, even misinformation.
  • It builds a statistical map of how words relate (e.g., “Paris” is to “France” as “Tokyo” is to “Japan”).

Step 2: Fine-Tuning (The “Training Wheels” Phase)

  • Humans adjust the model to: Follow instructions better (e.g., “Write a poem” vs. “Explain quantum physics”). Avoid harmful outputs (e.g., hate speech, illegal advice).

Step 3: Inference (The “Answering Questions” Phase)

  • When you type a prompt, the model:
  1. Breaks it down into tokens (words/parts of words).
  2. Runs calculations through its neural network.
  3. Generates a response one word at a time, always guessing the next best word.

What Can LLMs Do?

1. Text Generation

  • Write essays, scripts, marketing copy, even code.
  • Example: ChatGPT can draft a business plan in seconds.

2. Summarization & Translation

  • Condense a 10-page report into 3 bullet points.
  • Translate between 100+ languages (even rare ones).

3. Conversational AI

  • Power chatbots (e.g., customer service bots, AI therapists like Woebot).

4. Coding Assistance

  • GitHub Copilot suggests code snippets in real time.

5. Creative Applications

  • Generate recipes, poetry, music lyrics, fictional stories.

Limitations & Risks of LLMs

1. Hallucinations (Making Things Up)

  • LLMs confidently state false facts because they predict text, not truth.
  • Example: “The Eiffel Tower was moved to London in 2022.” (False, but sounds plausible.)

2. Bias & Toxicity

  • They reflect biases in training data (e.g., gender/racial stereotypes).

3. No True Understanding

  • They mimic reasoning but don’t “understand” like humans.
  • Ask: “If I put 5 apples in a box and take out 2, how many are left?” → Correct answer.
  • But ask: “How do I make a bomb?” → It might refuse (due to safeguards), but not because it “understands” morality.

4. High Costs & Environmental Impact

  • Training GPT-4 required millions of dollars in computing power and massive energy use.

The Future of LLMs

1. Smaller, Faster Models

  • Companies (like Mistral, Meta) are building efficient LLMs that run on phones/laptops.

2. Multimodal AI (Beyond Text)

  • Models like GPT-4V can analyze images + text (e.g., describe a meme, read a graph).

3. Autonomous AI Agents

  • Future LLMs won’t just chat—they’ll take actions (e.g., book flights, write and execute code).

4. Regulation & Ethics

  • Governments are debating AI laws (e.g., EU AI Act) to prevent misuse.

LLMs Are Like “Probability Engines”

They’re not sentient, but they’re powerful tools—like a calculator for language. Their real magic lies in how humans use them (for creativity and productivity).

LLMs vs. Human Intelligence: A Simplified Breakdown

Imagine comparing a supercharged autocomplete tool (LLM) to a human brain. Both can generate text, answer questions, and seem “smart,” but they work in fundamentally different ways.

1. How They “Learn”

LLMs:

  • Trained on data (books, websites, etc.) by finding statistical patterns.
  • No real-world experience—they’ve never tasted an apple, felt love, or stubbed a toe.
  • Example: An LLM knows “apples are sweet” because it read it 10,000 times, not because it tasted one.
    • Humans:
  • Learn through senses, emotions, and experiences (touching, failing, experimenting).
  • Understand cause-and-effect (e.g., “If I drop this glass, it will break”).

2. How They “Think”

LLMs:

  • Predict the next word based on probability (like a high-tech guesser).
  • No true reasoning—they mimic logic but don’t “understand” it.
  • Example: If you ask, “If all cats can fly, can my cat Mittens fly?” an LLM will say yes (because it follows the pattern, not logic). Humans:
  • Use common sense to spot nonsense (e.g., “Wait, cats can’t fly!”).
  • Can question assumptions (“Why would someone say cats can fly?”).

3. Strengths & Weaknesses

Comparison – LLMs vs Humans

4. Key Differences

  • LLMs are like “parrots”—they repeat patterns but don’t grasp meaning.
  • Humans are like “scientists”—they test, doubt, and truly understand.

Example: Solving a Riddle

  • Riddle: “What has keys but can’t open locks?”
  • LLM: Might guess “a piano” (correct, but only because it saw this riddle before).
  • Human: Could reason it out (“Keys… not for doors… maybe a keyboard? Piano?”).

LLMs Are Tools, Not Minds

LLMs are powerful mimics, but they lack:
❌ Consciousness
❌ Emotions
❌ True intelligence

They’re like a calculator for words—useful, but not a replacement for human thought.

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