#4078 Vocabulary Required to Document all Knowledge

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#4078 Vocabulary Required to Document all Knowledge

#4078 Since Webster's Third New International Dictionary, Unabridged, includes over 470,000 entries, what is a reasonable maximum number of unique words to define all knowledge?

Defining "all knowledge" is a philosophical mountain, but we can look at the linguistic "base camp" required to explain almost any concept. While Webster’s Third is a massive inventory of specialized terms, the number of words needed to define them is significantly smaller.

Here is a breakdown of how many unique words are reasonably required to map the landscape of human knowledge.

1. The "Defining Vocabulary" (2,000 – 3,000 words)

The most practical answer comes from lexicography. Major dictionaries (like Longman or Oxford) use a restricted Defining Vocabulary to explain all 400,000+ entries in their volumes.

  • The Logic: If you can define "Quantum Mechanics" using only 2,000 simple words, those 2,000 words technically "contain" the knowledge of the more complex term.

  • The Core: This set usually focuses on high-frequency verbs, logical connectors, and basic nouns.

2. The Semantic Primitives (60 – 100 words)

If you want the absolute "maximum-minimum," linguist Anna Wierzbicka proposed the Natural Semantic Metalanguage (NSM).

  • The Theory: All complex thoughts can be broken down into "primes"—concepts like I, you, someone, something, do, happen, good, bad.

  • The Number: There are roughly 65 universal primes. In theory, every piece of human knowledge can be reconstructed by combining these atoms of meaning.

3. The Functional Maximum (40,000 – 60,000 words)

While 3,000 words can define knowledge, they cannot express it with nuance or efficiency.

  • The Educated Native Speaker: Most university-educated adults have a vocabulary in this range.

  • The Threshold: This is generally considered the "ceiling" for unique words needed to discuss science, art, philosophy, and daily life without sounding like a technical manual or a toddler.

Comparison of Vocabulary Scales

ScaleWord CountPurpose
Semantic Primes~65The "atoms" of human thought.
Defining Vocabulary2,000 – 3,000Explaining any entry in a dictionary.
Functional Fluency10,000Comprehensive daily expression.
Total Knowledge50,000+High-level technical and cultural nuance.

The Verdict: If you want to define all knowledge efficiently, 2,500 words is the "sweet spot" used by linguists. If you want to express that knowledge with precision, you need closer to 50,000.

Would you like me to help you draft a description of a complex topic using only a limited "defining vocabulary" to see how it works?


Can a 50K vocabulary benefit new LLMs?

While the dictionary's 470,000 entries represent the vastness of the English language, a 50,000-word (50K) vocabulary is actually considered a "sweet spot" in the world of Large Language Models (LLMs).

In fact, many foundational models (like the original GPT-2 and Llama 1) used a vocabulary size of roughly 32K to 50K. Here is how a 50K vocabulary benefits new LLMs and why some models are now moving beyond it.

1. The Benefits of a 50K Vocabulary

Efficient "Subword" Coverage

LLMs don't typically learn whole words; they use subwords. A 50K vocabulary is large enough to include:

  • Common full words: "apple," "mountain," "philosophy."

  • Frequent sub-parts: "ing," "pre," "anti."

    This allows the model to "construct" any of the 470,000+ dictionary words it hasn't seen by combining these 50,000 building blocks.

Reduced "Sequence Length"

If a vocabulary is too small (e.g., only 5,000 words), the model has to break words into tiny pieces (like "p-h-i-l-o-s-o-p-h-y").

  • Memory Efficiency: A 50K vocabulary allows most words to be just 1 or 2 tokens long.

  • Context Window: Shorter sequences mean you can fit more information (more "knowledge") into the model's limited memory at once.

Balanced Computational Cost

Every word in the vocabulary requires a "slot" in the model's final layer.

  • Softmax Bottleneck: If the vocabulary is too large (say, 500,000), the model spends too much "brain power" just deciding which word to pick next.

  • 50K Sweet Spot: It provides enough variety to be articulate without making the model's "decision-making" layer too heavy and slow.

2. When 50K is Not Enough (The Modern Shift)

While 50K was the standard for years, newer models (like GPT-4o or Llama 3) are pushing toward 100K or even 256K. Here is why:

  • Multilingual Support: If you want a model to be equally good at English, Chinese, Arabic, and Hindi, 50K is too cramped. You need more "slots" to store unique characters and scripts without them overlapping and "confusing" the model.

  • Coding & Math: Specialized tokens for programming (like specific indentation patterns or rare operators) benefit from a larger vocabulary so the model doesn't have to "spell out" code character-by-character.

  • Compression: Larger vocabularies (100K+) can compress text by 25–30% compared to a 50K vocabulary, making the model faster and cheaper to run in the long term.

Summary Comparison

Vocabulary SizeStrategyBest For
32K – 50KEfficiencyMonolingual models, limited hardware, fast training.
100K – 150KPerformanceModern "Gold Standard" for high-quality English & Code.
200K+Global ReachMassive multilingual models (Gemma, Llama 3).

The Verdict: A 50K vocabulary is an excellent, "reasonable" foundation for a new LLM focusing on a single language. It captures the "defining vocabulary" of all knowledge perfectly. However, for a "god-tier" model that understands the world's many languages and complex code, 100K+ is the new target.

Would you like to see how a 50K vocabulary would "tokenize" a specific complex sentence compared to a smaller 5K vocabulary?

From <https://gemini.google.com/app/>  Google Gemini 3.0 Pro (Thinking)

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