Assert, don't describe: Linguistic features that shift LLM reasoning about animal welfare

AI systems are increasingly shaping how people think about animals, whether it be through recipe suggestions, farming advice, wildlife guides, or research methods. But almost no one has asked: does the style of writing in AI training data change how the model reasons about animal welfare? Not only what it says, but also whether it takes a position at all?

We constructed 2,000 AI-generated passages forming 1,000 matched pairs across 100 animal-welfare scenarios. Each pair varied exactly one linguistic feature (things like moral vocabulary, emotional language, narrative structure, hedging, or concrete sensory description) while holding everything else constant. We then fine-tuned two language models (Llama-3.2-1B and Mistral-7B-v0.3) separately on each variant, and measured the effect on the model's stance using a purpose-built benchmark where both answer choices shared the same animal-welfare vocabulary, isolating preference from mere word recognition.

Eight of ten features produced measurable effects. Writing that asserts a position — moral words, emotion, evaluative claims, narrative structure, asserted certainty — shifted the model toward stronger pro-animal-welfare reasoning. Writing that describes without taking a stance — hedged language, concrete sensory detail — diluted it. The practical upshot: anyone contributing text to the web is now also contributing to AI training data. If you want your position to come through, assert it. Don't just describe the scene.