| 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. |
Similar Papers
Bias Analysis and Mitigation in the Evaluation of Authorship Verification (P19-1)
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| Challenge: | a paper on authorship verification shows that the underlying experiment design cannot guarantee pushing forward the state of the art. |
| Approach: | They propose a "Basic and Fairly Flawed" authorship verifier that is on a par with the best approaches submitted so far . they pinpoint sources of bias that should be eliminated and propose 'refined' authorship corpus as effective countermeasure. |
| Outcome: | The proposed approach is on par with the best approaches submitted so far . the proposed approach shows that sources of bias should be eliminated . |
Learning Universal Authorship Representations (2021.emnlp-main)
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Rafael A. Rivera-Soto, Olivia Elizabeth Miano, Juanita Ordonez, Barry Y. Chen, Aleem Khan, Marcus Bishop, Nicholas Andrews
| Challenge: | authorship verification has traditionally relied on modeling stylometric linguistic properties . but neural methods introduce a tradeoff: they obviate the need for manual feature design . |
| Approach: | They propose to use domain-specific features to improve authorship representations . they propose to study Amazon reviews, fanfiction short stories, and Reddit comments . |
| Outcome: | The proposed methods outperform existing methods in large-scale authorship verification scenarios. |
Layered Insights: Generalizable Analysis of Human Authorial Style by Leveraging All Transformer Layers (2025.emnlp-main)
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| Challenge: | Existing approaches to authorship attribution model only learn from the output layer of pre-trained transformers, ignoring representations learned at other layers. |
| Approach: | They propose a model that leverages the various linguistic representations learned at different layers of pre-trained transformer-based models to model the authorship attribution task more effectively. |
| Outcome: | The proposed model performs better on out-of-domain and in-domain scenarios, while ignoring representations learned at other layers. |
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. |
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. |
Topic or Style? Exploring the Most Useful Features for Authorship Attribution (C18-1)
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| Challenge: | Existing approaches to authorship attribution rely on individual's writing style and/or preferred topics. |
| Approach: | They analyse four widely used datasets to explore how different types of features affect authorship attribution accuracy under varying conditions. |
| Outcome: | The proposed model outperforms the state-of-the-art on two out of the four datasets used. |
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. |
An Empirical Study of Building a Strong Baseline for Constituency Parsing (P18-2)
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| Challenge: | Sequence-to-sequence models have been used for natural language generation tasks such as machine translation and summarization. |
| Approach: | They propose to build a strong baseline based on general purpose sequence-to-sequence models for constituency parsing. |
| Outcome: | The proposed model outperforms existing models in natural language generation tasks without any explicit task-specific knowledge or architecture of constituent parsing. |
Latent Space Interpretation for Stylistic Analysis and Explainable Authorship Attribution (2025.coling-main)
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| Challenge: | Recent authorship attribution methods learn authorship representations of text in a latent, uninterpretable space, which hinders their usability in real-world applications. |
| Approach: | They propose a method for interpreting latent authorship representations by identifying representative points in the latent space and leveraging large language models to generate informative natural language descriptions of the writing style associated with each point. |
| Outcome: | The proposed method outperforms baseline methods on the authorship attribution task by +20% on average when aided with explanations from the method. |
The Two Paradigms of LLM Detection: Authorship Attribution vs Authorship Verification (2025.findings-acl)
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| Challenge: | Existing methods for detecting texts generated by large language models are disputed . authors argue that there are limitations in the current technology . |
| Approach: | They propose to make LLM detectors robust against domain shifts and build benchmarks . they argue that the limitations lie elsewhere, and open the realm of authorship analysis technology . |
| Outcome: | The proposed method systematically analyzes the benchmarks and validates it using state-of-the-art detectors. |