# Can Large Language Models Identify Authorship?

arXiv: https://arxiv.org/abs/2403.08213 · HTML: https://mlsanity.com/paper/2403.08213/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 68 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://mlsanity.com/paper/2210.17323/) (2022)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [Evaluating the Social Impact of Generative AI Systems in Systems and Society](https://mlsanity.com/paper/2306.05949/) (2023)
- [Same Author or Just Same Topic? Towards Content-Independent Style Representations](https://mlsanity.com/paper/2204.04907/) (2022)
- [A Survey on Detection of LLMs-Generated Content](https://mlsanity.com/paper/2310.15654/) (2023)
- [Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites](https://mlsanity.com/paper/2305.09820/) (2023)
- [Learning Interpretable Style Embeddings via Prompting LLMs](https://mlsanity.com/paper/2305.12696/) (2023)
- [Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements](https://mlsanity.com/paper/2001.00623/) (2020)
- [PART: Pre-trained Authorship Representation Transformer](https://mlsanity.com/paper/2209.15373/) (2022)
- [Similarity Learning for Authorship Verification in Social Media](https://mlsanity.com/paper/1908.07844/) (2019)
- [Neural Authorship Attribution: Stylometric Analysis on Large Language Models](https://mlsanity.com/paper/2308.07305/) (2023)
- [An Investigation of Supervised Learning Methods for Authorship Attribution in Short Hinglish Texts using Char & Word N-grams](https://mlsanity.com/paper/1812.10281/) (2018)
- [Forensic Authorship Analysis of Microblogging Texts Using N-Grams and Stylometric Features](https://mlsanity.com/paper/2003.11545/) (2020)

## Built on by (most cited first, 30 of 41)

- [Can Knowledge Editing Really Correct Hallucinations?](https://mlsanity.com/paper/2410.16251/) (2024)
- [Piecing It All Together: Verifying Multi-Hop Multimodal Claims](https://mlsanity.com/paper/2411.09547/) (2024)
- [Vid2Coach: Transforming How-To Videos into Task Assistants](https://mlsanity.com/paper/2506.00717/) (2025)
- [Catch Me If You Can? Not Yet: LLMs Still Struggle to Imitate the Implicit Writing Styles of Everyday Authors](https://mlsanity.com/paper/2509.14543/) (2025)
- [Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin](https://mlsanity.com/paper/2410.09245/) (2024)
- [Masks and Mimicry: Strategic Obfuscation and Impersonation Attacks on Authorship Verification](https://mlsanity.com/paper/2503.19099/) (2025)
- [InstructAV: Instruction Fine-tuning Large Language Models for Authorship Verification](https://mlsanity.com/paper/2407.12882/) (2024)
- [A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution](https://mlsanity.com/paper/2410.21716/) (2024)
- [Noise, Adaptation, and Strategy: Assessing LLM Fidelity in Decision-Making](https://mlsanity.com/paper/2508.15926/) (2025)
- [CAVE: Controllable Authorship Verification Explanations](https://mlsanity.com/paper/2406.16672/) (2024)
- [Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches](https://mlsanity.com/paper/2505.15422/) (2025)
- [Model Attribution in LLM-Generated Disinformation: A Domain Generalization Approach with Supervised Contrastive Learning](https://mlsanity.com/paper/2407.21264/) (2024)
- [Unraveling Interwoven Roles of Large Language Models in Authorship Privacy: Obfuscation, Mimicking, and Verification](https://mlsanity.com/paper/2505.14195/) (2025)
- [How Well Do LLMs Imitate Human Writing Style?](https://mlsanity.com/paper/2509.24930/) (2025)
- [Team "better_call_claude": Style Change Detection using a Sequential Sentence Pair Classifier](https://mlsanity.com/paper/2508.00675/) (2025)
- [Two Birds with One Stone: Multi-Task Detection and Attribution of LLM-Generated Text](https://mlsanity.com/paper/2508.14190/) (2025)
- [Robust Native Language Identification through Agentic Decomposition](https://mlsanity.com/paper/2509.16666/) (2025)
- [One-shot Style Transfer LLM log-probabilities for Authorship Attribution and Verification](https://mlsanity.com/paper/2510.13302/) (2025)
- [Cross-Genre Authorship Attribution via LLM-Based Retrieve-and-Rerank](https://mlsanity.com/paper/2510.16819/) (2025)
- [Individualized Cognitive Simulation in Large Language Models: Evaluating Different Cognitive Representation Methods](https://mlsanity.com/paper/2510.20252/) (2025)
- [From LLMs to Agents: A Comparative Evaluation of LLMs and LLM-based Agents in Security Patch Detection](https://mlsanity.com/paper/2511.08060/) (2025)
- [De-Anonymization at Scale via Tournament-Style Attribution](https://mlsanity.com/paper/2601.12407/) (2026)
- [Assessing Deanonymization Risks with Stylometry-Assisted LLM Agent](https://mlsanity.com/paper/2602.23079/) (2026)
- [Learning from Litigation: Graphs and LLMs for Retrieval and Reasoning in eDiscovery](https://mlsanity.com/paper/2405.19164/) (2024)
- [Stylometry recognizes human and LLM-generated texts in short samples](https://mlsanity.com/paper/2507.00838/) (2025)
- [A Computational Framework to Identify Self-Aspects in Text](https://mlsanity.com/paper/2507.13115/) (2025)
- [Comparative Personalization for Multi-document Summarization](https://mlsanity.com/paper/2509.21562/) (2025)
- [A Comparative Study of LLM Prompting and Fine-Tuning for Cross-genre Authorship Attribution on Chinese Lyrics](https://mlsanity.com/paper/2511.21930/) (2025)
- [Code Fingerprints: Disentangled Attribution of LLM-Generated Code](https://mlsanity.com/paper/2603.04212/) (2026)
- [The Conundrum of Trustworthy Research on Attacking Personally Identifiable Information Removal Techniques](https://mlsanity.com/paper/2603.08207/) (2026)

## Abstract (arXiv)

The ability to accurately identify authorship is crucial for verifying content authenticity and mitigating misinformation. Large Language Models (LLMs) have demonstrated an exceptional capacity for reasoning and problem-solving. However, their potential in authorship analysis remains under-explored. Traditional studies have depended on hand-crafted stylistic features, whereas state-of-the-art approaches leverage text embeddings from pre-trained language models. These methods, which typically require fine-tuning on labeled data, often suffer from performance degradation in cross-domain applications and provide limited explainability. This work seeks to address three research questions: (1) Can LLMs perform zero-shot, end-to-end authorship verification effectively? (2) Are LLMs capable of accurately attributing authorship among multiple candidates authors (e.g., 10 and 20)? (3) Can LLMs provide explainability in authorship analysis, particularly through the role of linguistic features? Moreover, we investigate the integration of explicit linguistic features to guide LLMs in their reasoning processes. Our assessment demonstrates LLMs' proficiency in both tasks without the need for domain-specific fine-tuning, providing explanations into their decision making via a detailed analysis of linguistic features. This establishes a new benchmark for future research on LLM-based authorship analysis.