Cross-Genre Authorship Attribution via LLM-Based Retrieve-and-Rerank

• URL: http://arxiv.org/abs/2510.16819v1
• Date: Sun, 19 Oct 2025 13:10:49 GMT
• Title: Cross-Genre Authorship Attribution via LLM-Based Retrieve-and-Rerank
• Authors: Shantanu Agarwal, Joel Barry, Steven Fincke, Scott Miller,
• Abstract summary: We introduce a two-stage retrieve-and-rerank framework that finetunes LLMs for cross-genre AA.<n>We achieve substantial gains of 22.3 and 34.4 absolute Success@8 points over the previous state-of-the-art on HIATUS's challenging HRS1 and HRS2 cross-genre AA benchmarks.
• Score: 4.263730995186838
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Authorship attribution (AA) is the task of identifying the most likely author of a query document from a predefined set of candidate authors. We introduce a two-stage retrieve-and-rerank framework that finetunes LLMs for cross-genre AA. Unlike the field of information retrieval (IR), where retrieve-and-rerank is a de facto strategy, cross-genre AA systems must avoid relying on topical cues and instead learn to identify author-specific linguistic patterns that are independent of the text's subject matter (genre/domain/topic). Consequently, for the reranker, we demonstrate that training strategies commonly used in IR are fundamentally misaligned with cross-genre AA, leading to suboptimal behavior. To address this, we introduce a targeted data curation strategy that enables the reranker to effectively learn author-discriminative signals. Using our LLM-based retrieve-and-rerank pipeline, we achieve substantial gains of 22.3 and 34.4 absolute Success@8 points over the previous state-of-the-art on HIATUS's challenging HRS1 and HRS2 cross-genre AA benchmarks.

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