Quantifying Culture from Embeddings to Generation

Abstract:

Measuring culture has long been a core sociological pursuit. The emergence of word embeddings in the 2010s offered a new approach to quantify culture by representing systems of meaning as mathematical relationships among concepts, allowing scholars to compare how these systems of meaning vary across social groups, historical periods, and community contexts. Recent advances in artificial intelligence have complicated the promise of embedding methods. Transformer-based language models incorporate vastly larger contexts and represent meaning dynamically, bringing computational models potentially closer to the scale and contextuality of human language use. Yet these advances have come at a cost. Contemporary foundation models aggregate information across languages, communities, and historical periods, producing representations that are increasingly detached from identifiable social standpoints. This creates a paradox for quantitative cultural research: models now track cultural information across much larger contexts than any human could, yet they are less clearly tied to the situated perspectives through which culture is experienced and learned by humans. This chapter traces the trajectory from static embeddings to contemporary large language models and examines the implications of this shift for the formal modeling of culture. We review how static embeddings were used to measure cultural associations and we consider emerging evidence that large generative models struggle to reproduce the diversity and variance observed in human populations. We then examine two provisional solutions: steering methods that attempt to recover latent perspectives from within large models, and historically or socially bounded models designed to preserve identifiable cultural standpoints. We conclude that the central challenge facing computational cultural sociology is determining whether perspective can be meaningfully modeled within increasingly powerful language models.

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Recommended citation: Nelson, Laura K. and Tom Einhorn. 2026. "Quantifying Culture from Embeddings to Generation". In Notes on the Future of Quantitative Social Science, edited by Rohan Alexander.