title: Authorship Attribution in the Era of LLMs: Problems, Methodologies, and
    Challenges
summary: 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
paper_authors._id: 66c413738c3816c5632419f3
paper_authors.name: Baixiang Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-28T11:27:18.564Z
paper_authors._id: 64821657564f4a586629a76a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64821657564f4a586629a76a/LoBNDQFD7yHiueESY14Ic.jpeg
paper_authors.fullname: Baixiang Huang
paper_authors.type: user
paper_authors.user: BaixHuang
paper_authors._id: 66c413738c3816c5632419f4
paper_authors.name: Canyu Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-22T19:51:44.028Z
paper_authors._id: 6483af58571c2dcfa98cae82
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6483af58571c2dcfa98cae82/d8Vnh_EG1KxeEoNAAQXHZ.jpeg
paper_authors.fullname: Canyu Chen
paper_authors.type: user
paper_authors.user: canyuchen
paper_authors._id: 66c413738c3816c5632419f5
paper_authors.name: Kai Shu
paper_title: Authorship Attribution in the Era of LLMs: Problems, Methodologies, and
    Challenges
paper_summary: 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
paper_ai_summary: 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.
paper_ai_keywords: Large Language Models (LLMs) Human-written Text Attribution LLM-generated Text Detection LLM-generated Text Attribution Human-LLM Co-authored Text Attribution generalization explainability