Tracing the Genealogies of Ideas with Large Language Model Embeddings
Lucian Li
In this paper, I present a novel method to detect intellectual influence
across a large corpus. Taking advantage of the unique affordances of large
language models in encoding semantic and structural meaning while remaining
robust to paraphrasing, we can search for substantively similar ideas and hints
of intellectual influence in a computationally efficient manner. Such a method
allows us to operationalize different levels of confidence: we can allow for
direct quotation, paraphrase, or speculative similarity while remaining open
about the limitations of each threshold. I apply an ensemble method combining
General Text Embeddings, a state-of-the-art sentence embedding method optimized
to capture semantic content and an Abstract Meaning Representation graph
representation designed to capture structural similarities in argumentation
style and the use of metaphor. I apply this method to vectorize sentences from
a corpus of roughly 400,000 nonfiction books and academic publications from the
19th century for instances of ideas and arguments appearing in Darwin's
publications. This functions as an initial evaluation and proof of concept; the
method is not limited to detecting Darwinian ideas but is capable of detecting
similarities on a large scale in a wide range of corpora and contexts.