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I Can Find You in Seconds! Leveraging Large Language Models for Code Authorship Attribution
arXiv.orgSource code authorship attribution is important in software forensics, plagiarism detection, and protecting software patch integrity. Existing techniques often rely on supervised machine learning, which struggles with generalization across different ...
Soohyeon Choi +6 more
semanticscholar +1 more source
AISec@CCS
As code generated by Large Language Models (LLMs) becomes more common, identifying the specific model behind each sample is increasingly important. This paper presents the first systematic study of LLM authorship attribution for C programs.
Tamás Bisztray +8 more
semanticscholar +1 more source
As code generated by Large Language Models (LLMs) becomes more common, identifying the specific model behind each sample is increasingly important. This paper presents the first systematic study of LLM authorship attribution for C programs.
Tamás Bisztray +8 more
semanticscholar +1 more source
Authorship Attribution in Multilingual Machine-Generated Texts
arXiv.orgAs Large Language Models (LLMs) have reached human-like fluency and coherence, distinguishing machine-generated text (MGT) from human-written content becomes increasingly difficult.
Lucio La Cava +4 more
semanticscholar +1 more source
The Two Paradigms of LLM Detection: Authorship Attribution vs Authorship Verification
Annual Meeting of the Association for Computational Linguistics,
Janek Bevendorff +4 more
semanticscholar +1 more source
Authorship Attribution of Microtext Using Capsule Networks
IEEE Transactions on Computational Social Systems, 2022Authorship attribution (AA) is an important task, as it identifies the author of a written text from a set of suspect authors. Different methodologies of anonymous writing have been discovered with the rising usage of social media. This anonymous writing
Chanchal Suman +3 more
semanticscholar +1 more source
Forged-GAN-BERT: Authorship Attribution for LLM-Generated Forged Novels
Conference of the European Chapter of the Association for Computational LinguisticsThe advancement of generative Large Language Models (LLMs), capable of producing human-like texts, introduces challenges related to the authenticity of the text documents.
Kanishka Silva +5 more
semanticscholar +1 more source
Authorship Attribution for Neural Text Generation
Conference on Empirical Methods in Natural Language Processing, 2020In recent years, the task of generating realistic short and long texts have made tremendous advancements. In particular, several recently proposed neural network-based language models have demonstrated their astonishing capabilities to generate texts ...
Adaku Uchendu +3 more
semanticscholar +1 more source
2017
A new effective system for identification and verification of text authorship has been developed. The system is created on the basis of machine learning. The originality of the model is caused by a suggested unique profile of the author’s style features.
Oleksandr Marchenko +4 more
openaire +1 more source
A new effective system for identification and verification of text authorship has been developed. The system is created on the basis of machine learning. The originality of the model is caused by a suggested unique profile of the author’s style features.
Oleksandr Marchenko +4 more
openaire +1 more source
arXiv.org
The task of deciding whether two documents are written by the same author is challenging for both machines and humans. This task is even more challenging when the two documents are written about different topics (e.g. baseball vs.
Steven Fincke, Elizabeth Boschee
semanticscholar +1 more source
The task of deciding whether two documents are written by the same author is challenging for both machines and humans. This task is even more challenging when the two documents are written about different topics (e.g. baseball vs.
Steven Fincke, Elizabeth Boschee
semanticscholar +1 more source
Authorship attribution using author profiling classifiers
Natural Language Engineering, 2022Authorship attribution – the computational task of identifying the author of a given text document within a set of possible candidates – has been attracting interest in Natural Language Processing research for many years.
C. Deutsch, Ivandré Paraboni
semanticscholar +1 more source