在LLM時代的作者歸屬：問題、方法論和挑戰

Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges

August 16, 2024

作者: Baixiang Huang, Canyu Chen, Kai Shu

cs.AI

摘要

準確歸因作者對於維護數位內容的完整性、改善法庭調查，以及減輕錯誤資訊和抄襲風險至關重要。解決正確歸因作者的迫切需求對於維護真實作者的可信度和責任至關重要。大型語言模型（LLMs）的快速進展已經模糊了人類和機器作者之間的界線，對傳統方法提出了重大挑戰。我們提出了一項全面的文獻綜述，探討了LLMs時代作者歸因研究的最新進展。這份調查系統地探索了這一領域的格局，通過將其分為四個代表性問題進行分類：（1）人類撰寫文本歸因；（2）LLM生成文本檢測；（3）LLM生成文本歸因；以及（4）人類-LLM共同撰寫文本歸因。我們還討論了與確保歸因方法的泛化性和可解釋性相關的挑戰。泛化性要求能夠跨越各種領域進行泛化，而可解釋性則強調提供對這些模型所做決策的透明和可理解的見解。通過評估現有方法和基準的優勢和局限性，我們確定了這一領域的關鍵開放問題和未來研究方向。這份文獻綜述為對這一快速發展領域感興趣的研究人員和從業人員提供了一份路線圖。額外資源和一份經過精心挑選的論文清單可在 https://llm-authorship.github.io 上獲得並定期更新。

English

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