HALvest-Contrastive: Retrieval-Like Authorship Attribution with Patch-Level Late Interaction

Abstract

Deciding whether two pieces of text share an author is made difficult by topical confound: two writers covering the same topic often look more alike than one writer covering two topics. We tackle this with HALvest, a 17-billion-token multilingual corpus of open-access scholarly papers, and its English contrastive derivative HALvest-Contrastive, in which same-author passages are drawn from distinct papers within a field to minimize topical overlap. We also revisit how documents are compared. Authorship systems traditionally compress each document into a single vector, we keep a sequence of vectors and compare them with late interaction, then introduce Patch-Level Late Interaction (PLI), which compresses neighboring tokens into patches before matching. Matching at the sequence level greatly improves performance over the single-vector baseline, but the optimal interaction granularity is subtle.