Papers with authorship attribution task
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. |
Knowing the Author by the Company His Words Keep (L18-1)
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| Challenge: | In traditional linguistics, there exists a famous saying that one should know a word by the company it keeps. |
| Approach: | They propose a method which uses word embeddings to identify pairwise relational features in the context of authorship attribution. |
| Outcome: | The proposed method is based on three literary corpora and shows that word similarity is a key feature in the authorship attribution task. |
A Unified Evaluation Framework for Novelty Detection and Accommodation in NLP with an Instantiation in Authorship Attribution (2023.findings-acl)
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| Challenge: | State-of-the-art natural language processing models have been shown to achieve remarkable performance in ‘closed-world’ settings where all the labels in the evaluation set are known at training time. |
| Approach: | They propose a multi-stage task to evaluate a system's performance on pipelined novelty ‘detection’ and ‘accommodation’ tasks. |
| Outcome: | The proposed model performs poorly in ‘closed-world’ settings where all the labels in the evaluation set are known at training time. |
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. |
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. |
IDTraffickers: An Authorship Attribution Dataset to link and connect Potential Human-Trafficking Operations on Text Escort Advertisements (2023.emnlp-main)
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| Challenge: | a significant number of human trafficking cases are associated with online advertisements . identification of HT vendors is challenging for law enforcement agencies . |
| Approach: | IDTraffickers uses 87,595 text ads and 5,244 vendor labels to link HT vendors . a macro-F1 score is achieved in a closed-set classification environment . |
| Outcome: | IDTraffickers is a dataset that enables verification and identification of HT vendors . the model achieves a macro-F1 score in a closed-set classification environment . |