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New research explores LLM-based and embedding strategies for zero-shot authorship attribution

By PulseAugur Editorial · [1 sources] ·

A new research paper explores strategies for zero-shot authorship attribution, a task that identifies an author without prior examples. The study compares LLM-based approaches with embedding-based methods, finding that label-only prompting is ineffective. Incorporating author-specific representations significantly improves performance, with a proposed two-stage LISA framework achieving the strongest results. While LLM-generated descriptions offer a more compact representation, they come at the cost of some attribution accuracy, indicating current open-source LLMs are insufficient for robust attribution without better representation learning. AI

IMPACT This research could lead to more effective AI-powered tools for verifying authorship and detecting AI-generated text.

RANK_REASON The cluster contains a research paper detailing new methods for authorship attribution. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research explores LLM-based and embedding strategies for zero-shot authorship attribution

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The cluster contains a research paper detailing new methods for authorship attribution. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Nudrat Habib, Tosin Adewumi, Sana Sabah Al-Azzawi, Marcus Liwicki, Elisa Barney ·

    Author Representation Strategies for Zero-Shot Authorship Attribution: A Comparative Study of LLM-Based and Embedding-Based Approaches

    arXiv:2610.03531v1 Announce Type: new Abstract: Authorship Attribution (AA) requires capturing fine-grained stylistic characteristics, making it particularly challenging in zero-shot (ZS) settings where no task-specific supervision is available. In this work, we investigate the e…

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