Authorship Attribution in the Era of LLMs: Problems, Methodologies, and
  Challenges
Accurate attribution of authorship is crucial for maintaining the integrity
of digital content, improving forensic investigations, and mitigating the risks
of misinformation and plagiarism. Addressing the imperative need for proper
authorship attribution is essential to uphold the credibility and
accountability of authentic authorship. The rapid advancements of Large
Language Models (LLMs) have blurred the lines between human and machine
authorship, posing significant challenges for traditional methods. We presents
a comprehensive literature review that examines the latest research on
authorship attribution in the era of LLMs. This survey systematically explores
the landscape of this field by categorizing four representative problems: (1)
Human-written Text Attribution; (2) LLM-generated Text Detection; (3)
LLM-generated Text Attribution; and (4) Human-LLM Co-authored Text Attribution.
We also discuss the challenges related to ensuring the generalization and
explainability of authorship attribution methods. Generalization requires the
ability to generalize across various domains, while explainability emphasizes
providing transparent and understandable insights into the decisions made by
these models. By evaluating the strengths and limitations of existing methods
and benchmarks, we identify key open problems and future research directions in
this field. This literature review serves a roadmap for researchers and
practitioners interested in understanding the state of the art in this rapidly
evolving field. Additional resources and a curated list of papers are available
and regularly updated at https://llm-authorship.github.io
66c413738c3816c5632419f3
Baixiang Huang
claimed_verified
2024-10-28T11:27:18.564Z
64821657564f4a586629a76a
https://cdn-avatars.huggingface.co/v1/production/uploads/64821657564f4a586629a76a/LoBNDQFD7yHiueESY14Ic.jpeg
Baixiang Huang
user
BaixHuang
66c413738c3816c5632419f4
Canyu Chen
claimed_verified
2024-08-22T19:51:44.028Z
6483af58571c2dcfa98cae82
https://cdn-avatars.huggingface.co/v1/production/uploads/6483af58571c2dcfa98cae82/d8Vnh_EG1KxeEoNAAQXHZ.jpeg
Canyu Chen
user
canyuchen
66c413738c3816c5632419f5
Kai Shu
Authorship Attribution in the Era of LLMs: Problems, Methodologies, and
  Challenges
Accurate attribution of authorship is crucial for maintaining the integrity
of digital content, improving forensic investigations, and mitigating the risks
of misinformation and plagiarism. Addressing the imperative need for proper
authorship attribution is essential to uphold the credibility and
accountability of authentic authorship. The rapid advancements of Large
Language Models (LLMs) have blurred the lines between human and machine
authorship, posing significant challenges for traditional methods. We presents
a comprehensive literature review that examines the latest research on
authorship attribution in the era of LLMs. This survey systematically explores
the landscape of this field by categorizing four representative problems: (1)
Human-written Text Attribution; (2) LLM-generated Text Detection; (3)
LLM-generated Text Attribution; and (4) Human-LLM Co-authored Text Attribution.
We also discuss the challenges related to ensuring the generalization and
explainability of authorship attribution methods. Generalization requires the
ability to generalize across various domains, while explainability emphasizes
providing transparent and understandable insights into the decisions made by
these models. By evaluating the strengths and limitations of existing methods
and benchmarks, we identify key open problems and future research directions in
this field. This literature review serves a roadmap for researchers and
practitioners interested in understanding the state of the art in this rapidly
evolving field. Additional resources and a curated list of papers are available
and regularly updated at https://llm-authorship.github.io
A literature review examines challenges and methods in authorship attribution for human and machine-generated texts, including detection and co-authored content, emphasizing generalization and explainability.
Large Language Models (LLMs) Human-written Text Attribution LLM-generated Text Detection LLM-generated Text Attribution Human-LLM Co-authored Text Attribution generalization explainability