What would you name your own LLM?

When considering a name for your own Large Language Model (LLM), the most effective approach is to select a name that reflects the model's purpose, intended personality, or underlying technical ethos. This decision should balance clarity, memorability, and uniqueness, while also aligning with your personal preferences or branding strategy.

Core Principles for a Strong LLM Name

• ·Distinctiveness: The name should be unique enough to stand out in search results, logs, and user memory. Avoid overly generic terms, which can lead to confusion. A unique proper noun is often the safest choice.
• ·Repeatability and Pronounceability: Users will frequently type and say the name. Aim for a name that is easy to recall, pronounce, and type, ideally less than 20 characters and without complex punctuation that might hinder command-line or API use. Good names are often one or two syllables.
• ·Scalability: Choose a name that won't become outdated or restrictive as your model evolves. Avoid embedding version numbers directly into the core brand name; these are best appended to instance names (e.g., "ModelX-v2").
• ·Brand Alignment: If the LLM has commercial or open-source aspirations, its name should subtly hint at its origin, purpose, or the creator's identity.
• ·Legal and Cultural Sensitivity: Conduct quick checks for existing trademarks (USPTO/WIPO) to prevent legal issues. Ensure the name doesn't carry unintended offensive or culturally insensitive connotations in any target language.
• ·Purpose Reflection: Directly or indirectly, the name should convey what the LLM is designed to do. This helps users quickly grasp its utility.

Effective Naming Strategies

1. ·

   Functional Naming (Clarity Over Creativity): These names directly communicate the model's utility or domain.

   • ·Examples: InsightGen (analytical), LinguaCraft (language/writing), CodeHelper (coding assistance), ResearchBuddy (research support).
   • ·Rationale: Provides immediate clarity on the tool's role.
2. ·

   Mythological or Historical References: These names evoke wisdom, innovation, or cultural significance, adding depth and memorability.

   • ·Examples: Athena (wisdom), Daedalus (invention), Curie (science/research), Mimir (Norse wisdom), Nereus (deep knowledge).
   • ·Rationale: Connects the LLM to esteemed traits or figures, but ensure the reference fits the model's capabilities.
3. ·

   Abstract or Artistic Names: These options highlight creativity, uniqueness, or an emotional connection.

   • ·Examples: Eloquence (persuasive language), Nexa (connection), Lumina (clarity/illumination), Nova (creativity/innovation), Spark (inspiration).
   • ·Rationale: Suitable for models focused on creative or interpersonal tasks.
4. ·

   Human-Centric Persona Names: These give the model a approachable, conversational guise.

   • ·Examples: Aria (melodic/creative), Cade (modern/techy), Jin (simple/intelligent), Lea (clean), Rhea (foundational), Silas (neutral/mysterious).
   • ·Rationale: Works well for customer-facing bots or interactive agents, adding a human touch.
5. ·

   Technical or Futuristic Names / Object-Oriented Technobabble: These emphasize innovation and technical sophistication.

   • ·Examples: NeuraLink (advanced neural architecture), SynthMind (synthetic intelligence), QuantumScribe (tech + creativity), Solar-Pro-X (domain/lineage), Nebula-Brain (multi-modal), Ether-Gen (speed/generation), Axiom-Net (reasoning), Meld-Core (fusion).
   • ·Rationale: Appeals to tech-savvy audiences, signaling advanced capabilities.
6. ·

   Personal or Sentimental Names: These reflect individuality or an emotional connection.

   • ·Examples: Sage (mentor), Aria (lyrical), Orion (robust/exploratory).
   • ·Rationale: Can add a unique, personal feel, but might be less suitable for professional, universally-branded LLMs.
7. ·

   Geographic or Cultural Nods: These show regional focus or ties to a community.

   • ·Examples: Hanul (Korean for sky), Bora (Korean for wind), Sakura (Japanese), Karak (Turkish for black stone).
   • ·Rationale: Useful for locally-sourced services or sovereign AI initiatives.
8. ·

   Acronyms or Alphanumeric Tags: Short, memorable, and often used in codebases, especially for internal tools or research pipelines.

   • ·Examples: XLML (eXtra-Large Model Learning), SAGE (Synthetic AI Generation Engine), AiBot-12, Model-X-2025, Aurora-v3.
   • ·Rationale: Useful for rapid parsing and versioning.

Practical Naming Checklist

• ·Target Audience: Who will interact with this LLM most frequently (developers, end-users, researchers)? This influences the tone and style of the name.
• ·Domain Clarity: Does the name offer any hint about the LLM's specialization (e.g., code, poetry, reasoning)?
• ·Legal Vetting: Always conduct a quick trademark search, especially if commercialization is a possibility.
• ·Domain Availability: If web hosting is planned, check if a clean subdomain or domain name is available for your chosen name.
• ·Future-Proofing: Ensure the name can adapt as the LLM's capabilities expand without sounding contradictory or limiting.

Names to Avoid

• ·Obvious Family Salts: Names like "GPT-4," "Claude-2," or "Gemini" can cause confusion and infringe on existing trademarks.
• ·Overly Generic Terms: "Assistant," "Bot," or simply "AI" lack distinctiveness for branding.
• ·Punctuation-Heavy or All-Caps: Names like "A.I." or "SUPER-INTUITIVE-BOT!" are difficult to type and can break command-line parsers.
• ·Culturally Taboo or Offensive Terms: These carry significant legal and PR risks.
• ·Naming After Living Persons Without Permission: This is a major legal and ethical pitfall.

Ultimately, a successful LLM name is one that is identifiable, memorable, and consistently associated with your model's unique capabilities.