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Authorship Attribution

Authorship attribution, also known as authorship identification, aims to attribute a previously unseen text of unknown authorship to one of a set of known authors.

Papers

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Showing 1–25 of 212 papers

TitleStatusHype
On the Origin of Llamas: Model Tree Heritage RecoveryCode2
Can Large Language Models Identify Authorship?Code1
Can Authorship Representation Learning Capture Stylistic Features?Code1
RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style TransformationCode1
FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity MetricCode1
ALISON: Fast and Effective Stylometric Authorship ObfuscationCode1
Cracking Double-Blind Review: Authorship Attribution with Deep LearningCode1
On the State of the Art in Authorship Attribution and Authorship VerificationCode1
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text GenerationCode1
Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge DistillationCode1
Representation learning of writing styleCode1
A Call for Clarity in Contemporary Authorship Attribution Evaluation—0
A Linear-complexity Multi-biometric Forensic Document Analysis System, by Fusing the Stylome and Signature Modalities—0
ALMs: Authorial Language Models for Authorship Attribution—0
A Deep Context Grammatical Model For Authorship Attribution—0
Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective—0
A Unified Evaluation Framework for Novelty Detection and Accommodation in NLP with an Instantiation in Authorship Attribution—0
AI Content Self-Detection for Transformer-based Large Language Models—0
An Investigation of Supervised Learning Methods for Authorship Attribution in Short Hinglish Texts using Char & Word N-grams—0
An Image Analysis Approach to the Calligraphy of Books—0
An Off-the-shelf Approach to Authorship Attribution—0
Approximating Style by N-gram-based Annotation—0
Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators—0
A Lexical, Syntactic, and Semantic Perspective for Understanding Style in Text—0
A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution—0
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