Generating well-referenced and accurate Wikipedia articles has always been an important problem: Wikipedia has essentially become the Internet's encyclopedia of record, and hundreds of millions of people use it do understand the world.
But over the last decade Wikipedia has also become a critical source of training data for data-hungry text generation models. As a result, any shortcomings in Wikipedia’s content are at risk of being amplified by the text generation tools of the future. If one type of topic or person is chronically under-represented in Wikipedia’s corpus, we can expect generative text models to mirror — or even amplify — that under-representation in their outputs.
Through that lens, the project of Wikipedia article generation is about much more than it seems — it’s quite literally about setting the scene for the language generation systems of the future, and empowering humans to guide those systems in more robust ways.
That’s why I wanted to talk to Meta AI researcher Angela Fan, whose latest project is focused on generating reliable, accurate, and structured Wikipedia articles. She joined me to talk about her work, the implications of high-quality long-form text generation, and the future of human/AI collaboration on this episode of the TDS podcast.
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Intro music:
- Artist: Ron Gelinas
- Track Title: Daybreak Chill Blend (original mix)
- Link to Track: https://youtu.be/d8Y2sKIgFWc
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Chapters:

1:45 Journey into Meta AI
5:45 Transition to Wikipedia
11:30 How articles are generated
18:00 Quality of text
21:30 Accuracy metrics
25:30 Risk of hallucinated facts
30:45 Keeping up with changes
36:15 UI/UX problems
45:00 Technical cause of gender imbalance
51:00 Wrap-up