Small edits, large models: How Wikipedia advocacy shapes LLM values

Can a small group of volunteers shape how AI systems discuss animal welfare, just by editing Wikipedia? We show that they can. Wikipedia appears in nearly every major language model training dataset and is weighted more heavily than web-crawled text. The Pro-Animal Wikipedians (PAW), a group of advocates who add sourced animal welfare content to relevant articles, have made 125 edits across 115 pages. Using gradient-based data attribution (Bergson; Lucia and Belrose 2026; MAGIC; Ilyas and Engstrom 2025), we traced how these edits inuence language model behavior. TrackStar retrieval attribution on Llama 3.1 8B found that PAW-edited sections made up 68% of the highest-attributed documents for animal welfare queries (p < 0.0001) but only 52% for unrelated queries about the same companies (p = 0.53): the model links PAW content specifically to animal welfaretopics, not to the entities in general. MAGIC counterfactual inuence estimation on Llama3.2-1B, run across five random training-order seeds, gave the same picture even more sharply: in every single seed, the top-10 most inuential documents on animal welfare queries were all PAW edits (10 of 10, 5 of 5 seeds), while on general queries the same top-10 sat at chance (4-6 of 10). Mean PAW inuence exceeded mean control inuence on animal welfare queries with p < 0.0001 in every seed, an effect 6-30× larger than on general queries; leave subset-out validation gave Spearman ρ = 1.00 for all 10 runs. When we ne-tuned separatemodels on PAW content versus control content, each model performed better specifcally on the type of text it was trained on, with no crossover benet: the PAW-trained model cut perplexity on animal welfare text from 12.4 to 8.4, while the control-trained model cut perplexity on control text from 16.1 to 11.4. A small, coordinated Wikipedia editing campaign therefore measurably shapes how language models handle the topics those edits address, making Wikipedia editing a practical, low-cost way for advocacy organizations to infuence AI systems.