Gemma-4-31B is self-aware about slop
I’m generating a dataset based on this model’s abilities to counter slop in RLHF. But I thought you guys would probably appreciate a model that can help you refine your prompts. It’s also quite good at detecting AI v. Human content.
I asked it: “What types of names are indicative of AI-generated fiction?”
It returned:
> Identifying AI-generated fiction through names is less about a specific list of "forbidden words" and more about patterns of probability. Because Large Language Models (LLMs) predict the next most likely token, they tend to gravitate toward "statistically safe" names—names that appear frequently in the tropes and genres found in their training data. > > Here are the types of names that are often indicative of AI-generated fiction: > > ### 1. The "High Fantasy" Phonetic Soup > AI often generates fantasy names by blending common phonetic elements associated with "magical" races (like Elves or Ancient Humans). These names often feel "pretty" but lack linguistic consistency. > * The "L-R-V" Overload: A heavy reliance on liquid consonants and soft vowels to sound ethereal. > * Examples: Elara, Thalric, Lyra, Aerith, Kaelen, Sylas, Valerius. > * The Tell: If every character in a fantasy setting has a name that sounds like it belongs in a generic RPG, it may be AI. > > ### 2. The "Compound Word" Method (Noun-Noun) > AI loves to create names by combining two evocative English words. While humans do this too, AI does it with a predictable, "on-the-nose" quality. > * The "Cool" Combination: Pairing a nature word with a metal, weapon, or celestial object. > * Examples: Shadowstep, Silverleaf, Stormborn, Ironheart, Nightshade, Starfall. > * The Tell: When these are used as surnames or city names (The City of Dawnspire, The Kingdom of Frostreach), it often signals AI, as these are the most mathematically probable "epic" combinations. > > ### 3. The "Thesaurus" Aesthetic > AI often reaches for words that sound poetic or "elevated" but are overused in digital writing prompts. These names feel like they were chosen for their vibe rather than as a real name a person would have. > * Celestial/Atmospheric words: Nova, Aurora, Zenith, Aether, Celeste, Seraphina. > * The Tell: A character named "Aether" or "Zenith" in a story where the tone is otherwise mundane is a common AI hallmark. > > ### 4. The "Statistically Safe" Moderns > When writing contemporary fiction, AI tends to avoid names that are too eccentric or too dated. It sticks to the "top 50" lists of the last two decades. > * The "Stock" Characters: Oliver, Emily, Sophia, Liam, Noah, Ava. > * The Tell: A cast of characters that feels like a generic 2010s classroom. There is a lack of "ugly" names, regional dialect names, or culturally specific names unless the prompt explicitly demanded them. > > ### 5. The "On-the-Nose" Symbolic Name > AI often struggles with subtlety. It frequently assigns names that directly describe the character's personality or role in the story. > * The Literalists: A cold character named Frost; a strong character named Stone; a mysterious character named Shadow; a pure character named Lily. > * The Tell: When the name serves as a spoiler for the character's personality, it reflects the AI's tendency to follow the most direct semantic path. > > ### Summary Table: Human vs. AI Naming Tendencies > > | Feature | Human Writer | AI Model | > | :--- | :--- | :--- | > | Phonetics | Often idiosyncratic or based on real linguistics. | Based on "generic" genre sounds (e.g., "Elvish"). | > | Diversity | Mixes common, rare, and "ugly" names. | Tends toward "pretty" or "statistically common" names. | > | Symbolism | Often subtle or ironic. | Often literal and descriptive. | > | World-building | Names often follow a consistent internal logic. | Names are "vibe-consistent" but linguistically random. | > > The Ultimate Tell: The biggest red flag isn't one single name, but a cluster. If you see a character named Elara Shadowstep living in the city of Silverpeak, you are almost certainly reading AI-generated text.