# Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges

arXiv: https://arxiv.org/abs/2408.08946 · HTML: https://mlsanity.com/paper/2408.08946/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2024.

## Built on by (most cited first, 30 of 47)

- [Idiosyncrasies in Large Language Models](https://mlsanity.com/paper/2502.12150/) (2025)
- [Can Knowledge Editing Really Correct Hallucinations?](https://mlsanity.com/paper/2410.16251/) (2024)
- [Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards](https://mlsanity.com/paper/2501.07493/) (2025)
- [Depth Gives a False Sense of Privacy: LLM Internal States Inversion](https://mlsanity.com/paper/2507.16372/) (2025)
- [Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasing](https://mlsanity.com/paper/2502.15208/) (2025)
- [Piecing It All Together: Verifying Multi-Hop Multimodal Claims](https://mlsanity.com/paper/2411.09547/) (2024)
- [Detecting Stylistic Fingerprints of Large Language Models](https://mlsanity.com/paper/2503.01659/) (2025)
- [I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution](https://mlsanity.com/paper/2506.17323/) (2025)
- [StoryScope: Investigating idiosyncrasies in AI fiction](https://mlsanity.com/paper/2604.03136/) (2026)
- [Authorship Attribution in Multilingual Machine-Generated Texts](https://mlsanity.com/paper/2508.01656/) (2025)
- [NoisePrints: Distortion-Free Watermarks for Authorship in Private Diffusion Models](https://mlsanity.com/paper/2510.13793/) (2025)
- [AIDBench: A benchmark for evaluating the authorship identification capability of large language models](https://mlsanity.com/paper/2411.13226/) (2024)
- [Model Misalignment and Language Change: Traces of AI-Associated Language in Unscripted Spoken English](https://mlsanity.com/paper/2508.00238/) (2025)
- [Residualized Similarity for Faithfully Explainable Authorship Verification](https://mlsanity.com/paper/2510.05362/) (2025)
- [Unveiling Large Language Models Generated Texts: A Multi-Level Fine-Grained Detection Framework](https://mlsanity.com/paper/2410.14231/) (2024)
- [Robust and Fine-Grained Detection of AI Generated Texts](https://mlsanity.com/paper/2504.11952/) (2025)
- [Distinguishing AI-Generated and Human-Written Text Through Psycholinguistic Analysis](https://mlsanity.com/paper/2505.01800/) (2025)
- [Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches](https://mlsanity.com/paper/2505.15422/) (2025)
- [The Attribution Crisis in LLM Search Results](https://mlsanity.com/paper/2508.00838/) (2025)
- [Tracing the Data Trail: A Survey of Data Provenance, Transparency and Traceability in LLMs](https://mlsanity.com/paper/2601.14311/) (2026)
- [Beyond checkmate: exploring the creative chokepoints in AI text](https://mlsanity.com/paper/2501.19301/) (2025)
- [Unraveling Interwoven Roles of Large Language Models in Authorship Privacy: Obfuscation, Mimicking, and Verification](https://mlsanity.com/paper/2505.14195/) (2025)
- [The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution](https://mlsanity.com/paper/2510.10493/) (2025)
- [Who Wrote the Book? Detecting and Attributing LLM Ghostwriters](https://mlsanity.com/paper/2603.28054/) (2026)
- [Isolating authorship from content with semantic embeddings and contrastive learning](https://mlsanity.com/paper/2411.18472/) (2024)
- [What's in a prompt? Language models encode literary style in prompt embeddings](https://mlsanity.com/paper/2505.17071/) (2025)
- [Better Call Claude: Can LLMs Detect Changes of Writing Style?](https://mlsanity.com/paper/2508.00680/) (2025)
- [Robust Native Language Identification through Agentic Decomposition](https://mlsanity.com/paper/2509.16666/) (2025)
- [De-Anonymization at Scale via Tournament-Style Attribution](https://mlsanity.com/paper/2601.12407/) (2026)
- [When New Generators Arrive: Lifelong Machine-Generated Text Attribution via Ridge Feature Transfer](https://mlsanity.com/paper/2606.05626/) (2026)

## Abstract (arXiv)

Accurate attribution of authorship is crucial for maintaining the integrity of digital content, improving forensic investigations, and mitigating the risks of misinformation and plagiarism. Addressing the imperative need for proper authorship attribution is essential to uphold the credibility and accountability of authentic authorship. The rapid advancements of Large Language Models (LLMs) have blurred the lines between human and machine authorship, posing significant challenges for traditional methods. We present a comprehensive literature review that examines the latest research on authorship attribution in the era of LLMs. This survey systematically explores the landscape of this field by categorizing four representative problems: (1) Human-written Text Attribution; (2) LLM-generated Text Detection; (3) LLM-generated Text Attribution; and (4) Human-LLM Co-authored Text Attribution. We also discuss the challenges related to ensuring the generalization and explainability of authorship attribution methods. Generalization requires the ability to generalize across various domains, while explainability emphasizes providing transparent and understandable insights into the decisions made by these models. By evaluating the strengths and limitations of existing methods and benchmarks, we identify key open problems and future research directions in this field. This literature review serves as a roadmap for researchers and practitioners interested in understanding the state of the art in this rapidly evolving field.