mlsanityData to 7 Oct 2026

InstructAV: Instruction Fine-tuning Large Language Models for Authorship Verification

arXiv 2407.12882 ↗First posted 16 Jul 2024cs.CL cs.AI cs.LGcited by 4

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Abstract (arXiv)

Large Language Models (LLMs) have demonstrated remarkable proficiency in a wide range of NLP tasks. However, when it comes to authorship verification (AV) tasks, which involve determining whether two given texts share the same authorship, even advanced models like ChatGPT exhibit notable limitations. This paper introduces a novel approach, termed InstructAV, for authorship verification. This approach utilizes LLMs in conjunction with a parameter-efficient fine-tuning (PEFT) method to simultaneously improve accuracy and explainability. The distinctiveness of InstructAV lies in its ability to align classification decisions with transparent and understandable explanations, representing a significant progression in the field of authorship verification. Through comprehensive experiments conducted across various datasets, InstructAV demonstrates its state-of-the-art performance on the AV task, offering high classification accuracy coupled with enhanced explanation reliability.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 35 references that are in the map. First posted on arXiv 16 Jul 2024.

Builds on

22 of 35 references.

References · 22

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10 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025How Well Do LLMs Imitate Human Writing Style?cited by 2
  2. 2026GerAV: Towards New Heights in German Authorship Verification using Fine-Tuned LLMs on a New Benchmarkcited by 1
  3. 2026Interpreting Style Representations via Style-Eliciting Promptscited by 0
  4. 2026When Writing Style Drifts: Benchmarking Authorship Verification under Distribution Shifts in Genre, Time and the AI-Eracited by 0