Assert, don't describe: Linguistic features that shift LLM reasoning about animal welfare
A new study finds that using linguistic features such as assertive certainty, explicit moral vocabulary, and emotion words in fine-tuning data significantly shifts LLM reasoning toward stronger pro-animal-welfare stances, while hedged language and concrete sensory description dilute that stance. The research offers practical guidance for animal-welfare advocates.
[2606.26104] Assert, don't describe: Linguistic features that shift LLM reasoning about animal welfare
[Submitted on 30 Apr 2026]
Title:Assert, don't describe: Linguistic features that shift LLM reasoning about animal welfare
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Abstract:Animal-welfare advocates produce a lot of writing, and increasingly that writing trains the language models that millions of people then ask about animal welfare. Using vocabulary-matched stance-contrast probes on a held-out animal-welfare benchmark, we measure how each of ten linguistic features changes Llama-3.2-1B's preference for pro-animal-welfare reasoning when used as fine-tuning data. Eight of the ten features produce statistically significant shifts. Seven move the model toward stronger pro-animal-welfare reasoning: assertive certainty, explicit moral vocabulary, emotion words, evaluative claims, narrative structure, depicted harm severity, and immediate temporal framing. Two move it the other way: hedged language and concrete sensory description both dilute the pro-animal-welfare stance. First-person perspective has no statistically significant effect. The practical recommendation for anyone writing animal-welfare text that may end up in LLM training corpora: assert a position rather than describe a scene neutrally. The features that shift the model are the ones that make the writer's position explicit; the features that dilute it hold animal-welfare content but withhold stance.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.26104 [cs.CL]
(or arXiv:2606.26104v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.26104
arXiv-issued DOI via DataCite
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From: Jasmine Brazilek [view email] [v1] Thu, 30 Apr 2026 23:59:56 UTC (286 KB)
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