| Challenge: | Authorship Verification (AV) is used for tasks such as plagiarism detection, forensic analysis, analysis of the spread of misinformation. |
| Approach: | They propose to train an offline authorship verification model that is accessible and easy to use. |
| Outcome: | The proposed model generates high quality explanations and competitive task accuracy on three difficult AV datasets. |
Similar Papers
Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification (2023.findings-emnlp)
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| Challenge: | Existing AV techniques, including stylometric and deep learning, face limitations in terms of data requirements and lack of explainability. |
| Approach: | They propose a technique that leverages Large-Language Models (LLMs) to provide step-by-step stylometric explanation prompts to verify authorship. |
| Outcome: | The proposed technique outperforms state-of-the-art baselines, operates effectively with limited training data, and enhances interpretability through intuitive explanations. |
The Million Authors Corpus: A Cross-Lingual and Cross-Domain Wikipedia Dataset for Authorship Verification (2025.findings-acl)
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| Challenge: | Authorship verification (AV) is a crucial task for identity verification, accountlinking, historical linguistics, and AI-generated text identification. |
| Approach: | They propose to use Wikipedia's Million Authors Corpus to examine authorship verification models on a broad scale. |
| Outcome: | The proposed dataset includes 60.08M textual chunks, contributed by 1.29M Wikipedia authors. |
Unraveling Interwoven Roles of Large Language Models in Authorship Privacy: Obfuscation, Mimicking, and Verification (2025.emnlp-main)
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| Challenge: | Recent advances in large language models have been driven by large-scale training corpora drawn from diverse sources such as websites, news articles, and books. |
| Approach: | They propose a framework for analyzing dynamic relationships among LLM-enabled AO, AM, and AV in the context of authorship privacy. |
| Outcome: | The proposed framework analyzes the dynamic relationships among LLM-enabled AO, AM, and AV in the context of authorship privacy. |
GerAV: Towards New Heights in German Authorship Verification using Fine-Tuned LLMs on a New Benchmark (2026.findings-acl)
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| Challenge: | Authorship verification (AV) is a task of determining whether two texts were written by the same author. |
| Approach: | They propose a benchmark for German AV comprising over 400k labeled text pairs. |
| Outcome: | The proposed model outperforms baselines and state-of-the-art models by 0.09 and surpasses GPT-5 in a zero-shot setting by 0.08. |
Who’s the Author? How Explanations Impact User Reliance in AI-Assisted Authorship Attribution (2025.findings-emnlp)
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| Challenge: | despite growing interest in explainable NLP, it remains unclear how explanation strategies shape user behavior in tasks like authorship identification. |
| Approach: | They propose two explanation types to support their analysis of user behavior . they use example-based style rewrites and feature-based rationales to generate explanations . |
| Outcome: | The proposed explanations support appropriate reliance, whereas explanations increase AI overreliance, the study finds . |
BEDAA: Bayesian Enhanced DeBERTa for Uncertainty-Aware Authorship Attribution (2025.findings-acl)
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| Challenge: | Existing methods for authorship attribution struggle with trustworthiness and interpretability across domains, languages, and stylistic variations. |
| Approach: | They propose a Bayesian-Enhanced DeBERTa framework that integrates Bayes' reasoning with transformer-based language models to enable uncertainty-aware authorship attribution. |
| Outcome: | The proposed framework achieves 19.69% improvement in F1-score across multiple authorship attribution tasks, including binary, multiclass, and dynamic authorship detection. |
Rethinking the Authorship Verification Experimental Setups (2022.emnlp-main)
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| Challenge: | Identifying the author of a text is one of the most versatile NLP tasks, with applications ranging from plagiarism detection to forensics and monitoring the activity of cyber-criminals. |
| Approach: | They propose five new public splits over the PAN dataset to isolate and identify biases related to the text topic and to the author’s writing style. |
| Outcome: | The proposed models are competitive with state-of-the-art methods and generalize better on dark reddit datasets. |
Open-World Authorship Attribution (2025.findings-acl)
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| Challenge: | Existing benchmarks for large language models do not evaluate their performance in academic research . authors aim to identify authors from anonymous text without additional information . |
| Approach: | They propose a benchmark to quantitatively assess LLMs' ability to infer author from text . they propose 'open-world' authorship attribute' to be a two-stage framework . |
| Outcome: | The proposed approach achieves 60.7% accuracy and 44.3% accuracy in two stages. |
Residualized Similarity for Faithfully Explainable Authorship Verification (2025.findings-emnlp)
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Peter Zeng, Pegah Alipoormolabashi, Jihu Mun, Gourab Dey, Nikita Soni, Niranjan Balasubramanian, Owen Rambow, H. Schwartz
| Challenge: | Neural methods achieve high accuracy, but their representations lack direct interpretability. |
| Approach: | They propose a method that supplements systems using interpretable features with a neural network to improve their performance while maintaining interpretability. |
| Outcome: | The proposed method improves the performance of state-of-the-art models while maintaining interpretability. |
Can Large Language Models Identify Authorship? (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional capacity for reasoning and problem-solving, but their potential in authorship analysis remains under-explored. |
| Approach: | They propose to integrate explicit linguistic features into LLMs to provide explanations into their reasoning processes. |
| Outcome: | The proposed models demonstrate their ability to perform zero-shot, end-to-end authorship verification effectively and provide explainability through explicit linguistic features. |