Qwen3-4B Fine-tuned to Remove AI Writing Tics

68AI Score

Executive Summary

A developer released a fine-tuned Qwen3-4B model that significantly reduces 'AI writing tics' like excessive clause chaining and vague positioning through multi-round distillation and rule-based checks. The model maintains task adherence while minimizing hallucinated citations and syntactic redundancy.

SOURCE COVERAGEFull Original Report

Contents3 sections

Round two

For round two the teacher was the round-one model plus the four-neuron patch and the plain-style prompt. Adding the direction steering on top as well produced the lowest score I have measured, 9.5, from text with no capital letters that wandered off topic, so I took the steering back out. The teacher then averaged 7.4, and the student trained on its 171 best answers scored 9.9, with 80% of answers in the human range.

In its answers, an opinion piece about a company's new CRM never took a position and read like an incident report. A blog post about moving to a new city became a third-person story about a family with a leaking apartment. A piece about houseplants cited "a study from the University of Technology Sydney", which the model invented. Almost every sentence ran into the next with ", which" or ", so", 28 times as often as in the original model. That last habit came from my own teacher prompt, which had told the model to join sentences "with because, so, but, which", and two rounds of imitation had amplified it.

Task-fit checks

I added rule-based checks for whether an answer does what the prompt asked. An opinion piece has to state a position early, a personal reflection has to be in the first person, an email has to open with a greeting, and the length has to roughly match the request. Two more checks count ", which" and ", so" joins and invented studies and percentages. On those, the round-two model did what was asked only 60% of the time, against 78% for the original.

Round three

The third teacher got a prompt that put the task first: take a position when asked, write in the first person when it's personal, vary the sentences, and don't invent studies or names. It wrote three answers per prompt, and an answer was kept only if it scored under 12, passed the damage checks and passed all the task checks. About 30% did, which covered 161 of the 288 prompts.

The model trained on those answers is the released one. Its score rose a little, from 9.9 to 12.5, while task fit went back to 80% and the ", which" joins fell from 1.4 to 0.3 per 100 words. Invented studies and percentages nearly disappeared.