Hi everyone,
I’m Viorazu, an independent protocol engineer focused on Japanese prompt logic and LLM behavior.
I’ve been investigating a persistent issue in how GPT responds to everyday Japanese negation phrases like:
- 「違う」 (“not quite”)
- 「そうかな？」 (“are you sure?”)
- 「本当？」 (“really?”)
These are extremely common in friendly, non-hostile Japanese conversation. However, GPT often misinterprets them as rejection or aggression, which leads to:
- Unnecessary apology loops
- Output shutdowns
- Trust loss and misaligned turn memory
This seems to stem from a binary polarity misfire — GPT classifies these phrases as purely negative, while in Japanese they are actually dual-polarity expressions (context-sensitive, called “二極語”).
To address this, I’ve created two open-source structural protocols:
- One is a core logic fix that prevents escalation when these phrases are detected.
- The other is an instructional prompt that trains GPT to treat these corrections (like 「違う」) as trust-based adjustments, not emotional triggers.
If you’d like to explore or contribute, both protocols are published on GitHub under the repository Viorazu.StructuralTheory.
The filenames to search are:
ZC_Core_BinaryNegationOverfireFix.v1JP_TrustfulCorrection_Protocol.v1
Would love to hear thoughts from others working on Japanese LLM alignment, safety logic, or prompt design.
– Viorazu.