# Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches

arXiv: https://arxiv.org/abs/2505.15422 · HTML: https://mlsanity.com/paper/2505.15422/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 180 references that are in the map.
- First posted on arXiv 21 May 2025.

## Builds on (most cited first, 30 of 35)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends](https://mlsanity.com/paper/2301.05712/) (2023)
- [Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges](https://mlsanity.com/paper/2408.08946/) (2024)
- [Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective](https://mlsanity.com/paper/2210.10488/) (2022)
- [Can Large Language Models Identify Authorship?](https://mlsanity.com/paper/2403.08213/) (2024)
- [Explainable Authorship Verification in Social Media via Attention-based Similarity Learning](https://mlsanity.com/paper/1910.08144/) (2019)
- [Training-free LLM-generated Text Detection by Mining Token Probability Sequences](https://mlsanity.com/paper/2410.06072/) (2024)
- [Understanding Convolutional Neural Networks with A Mathematical Model](https://mlsanity.com/paper/1609.04112/) (2016)
- [On the State of the Art in Authorship Attribution and Authorship Verification](https://mlsanity.com/paper/2209.06869/) (2022)
- [Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore](https://mlsanity.com/paper/2405.04286/) (2024)
- [A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts](https://mlsanity.com/paper/2311.08374/) (2023)
- [Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification](https://mlsanity.com/paper/2310.08123/) (2023)
- [On the Limitations of Large Language Models (LLMs): False Attribution](https://mlsanity.com/paper/2404.04631/) (2024)
- [Siamese Networks for Large-Scale Author Identification](https://mlsanity.com/paper/1912.10616/) (2019)
- [Understanding writing style in social media with a supervised contrastively pre-trained transformer](https://mlsanity.com/paper/2310.11081/) (2023)
- [Assessing the Applicability of Authorship Verification Methods](https://mlsanity.com/paper/1906.10551/) (2019)
- [Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin](https://mlsanity.com/paper/2410.09245/) (2024)
- [A Self-supervised Representation Learning of Sentence Structure for Authorship Attribution](https://mlsanity.com/paper/2010.06786/) (2020)
- [Cracking Double-Blind Review: Authorship Attribution with Deep Learning](https://mlsanity.com/paper/2211.07467/) (2022)
- [Rethinking the Authorship Verification Experimental Setups](https://mlsanity.com/paper/2112.05125/) (2021)
- [Authorship Attribution in Bangla literature using Character-level CNN](https://mlsanity.com/paper/2001.05316/) (2020)
- [A comparison of several AI techniques for authorship attribution on Romanian texts](https://mlsanity.com/paper/2211.05180/) (2022)
- [A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution](https://mlsanity.com/paper/2410.21716/) (2024)
- [T5 meets Tybalt: Author Attribution in Early Modern English Drama Using Large Language Models](https://mlsanity.com/paper/2310.18454/) (2023)
- [ALMs: Authorial Language Models for Authorship Attribution](https://mlsanity.com/paper/2401.12005/) (2024)

## Built on by (most cited first, 3 of 3)

- [From the Rock Floor to the Cloud: A Systematic Survey of State-of-the-Art NLP in Battery Life Cycle](https://mlsanity.com/paper/2510.27369/) (2025)
- [AuthBench: A Large-Scale Multilingual Benchmark for Authorship Representation across Genres and Lengths](https://mlsanity.com/paper/2609.06771/) (2026)
- [Author Representation Strategies for Zero-Shot Authorship Attribution: A Comparative Study of LLM-Based and Embedding-Based Approaches](https://mlsanity.com/paper/2610.03531/) (2026)

## Abstract (arXiv)

Authorship analysis plays an important role in diverse domains, including forensic linguistics, academia, cybersecurity, and digital content authentication. This paper presents a systematic literature review on two key sub-tasks of authorship analysis; Author Attribution and Author Verification. The review explores SOTA methodologies, ranging from traditional ML approaches to DL models and LLMs, highlighting their evolution, strengths, and limitations, based on studies conducted from 2015 to 2024. Key contributions include a comprehensive analysis of methods, techniques, their corresponding feature extraction techniques, datasets used, and emerging challenges in authorship analysis. The study highlights critical research gaps, particularly in low-resource language processing, multilingual adaptation, cross-domain generalization, and AI-generated text detection. This review aims to help researchers by giving an overview of the latest trends and challenges in authorship analysis. It also points out possible areas for future study. The goal is to support the development of better, more reliable, and accurate authorship analysis system in diverse textual domain.