Open-World Authorship Attribution (2025)

Abstract: Recent years have witnessed rapid advancements in Large Language Models (LLMs). Nevertheless, it remains unclear whether state-of-the-art LLMs can infer the author of an anonymous research paper solely from the text, without any additional information. To investigate this novel challenge, which we define as Open-World Authorship Attribution, we introduce a benchmark comprising thousands of research papers across various fields to quantitatively assess model capabilities. Then, at the core of this paper, we tailor a two-stage framework to tackle this problem: candidate selection and authorship decision. Specifically, in the first stage, LLMs are prompted to generate multi-level key information, which are then used to identify potential candidates through Internet searches. In the second stage, we introduce key perspectives to guide LLMs in determining the most likely author from these candidates. Extensive experiments on our benchmark demonstrate the effectiveness of the proposed approach, achieving 60.7% and 44.3% accuracy in the two stages, respectively. We will release our benchmark and source codes to facilitate future research in this field.

## introduction the advancement of generative artificial intelligence (ai) and large language models (llms) has revolutionized numerous fields due to their remarkable capabilities in natural language processing (nlp) tasks @xcite @xcite . despite their widespread applications, their potential for authorship attribution-the task of identifying an author from anonymous text-remains largely unexplored. in this paper, we investigate an intriguing question: can state-of-the-art llms infer the author of an anonymous research paper without any additional information? this problem is both practical and ambitious. on one hand, accurately attributing authorship in academic research is crucial for maintaining integrity, recognizing contributions, and detecting plagiarism or ghostwriting. on the other hand, directly applying modern llms to this task is challenging, as the relevant information is often dispersed across internet-scale data, which makes it infeasible for these models to process efficiently. in this paper, we define this challenging task as open-world authorship attribution. since no existing benchmark evaluates llm performance in this area, we construct a dataset comprising thousands of academic papers from various research fields. building on insights from this data, we propose a novel two-stage framework to address the task, including candidate selection and authorship decision. specifically, in the candidate-selection stage, we leverage llms to generate multi-level key representations of a target paper, which are then utilized to search the internet for relevant authors and their publications at multiple levels of specificity. the retrieved authors along with authors in the citation list form our candidate pool. in the authorshipdecision stage, llms assess potential authorship in the candidate pool by evaluating the anonymous text against multiple guidelines. finally, a holistic decision is made to determine the most probable author, serving as the final output. we conduct experiments using multiple stateof-the-art llms, including both open-source and closed-source models. extensive evaluation validate the effectiveness and the superiority of the proposed solution. specifically, our approach achieves 60.7% accuracy of candidate selection and 44.3% accuracy of authorship decision. the contribution of this work is summarized as below: ## methods in this section, we elaborate on the proposed benchmark and two-stage approach for open-world authorship attribution. sec. 3.1 introduces the data sources and the construction of the benchmark. secs. 3.2 and 3.3 describe the main pipelines of the two stages: candidate selection and authorship decision, respectively, in the proposed solution. in the first stage, candidate papers are retrieved from the internet using llm-generated keywords. in the second stage, llms determine the most likely author from these candidates. fig. 3 provides an overview of the entire streamline. ## conclusion in this paper, we introduce the first benchmark and dedicated solution for open-world authorship attribution. leveraging recent advancements in llms, we propose a two-stage pipeline: candidate selection and authorship decision. in the first stage, multi-levels keywords extracted from the target paper are used to search the internet. the retrieved results, combined with citation lists, form a pool of potential candidates. in the second stage, llms infer authorship based on writing style and citation similarity from these candidates. extensive experiments demonstrate the effectiveness and superiority of our approach over multiple potential baseline methods.