Ghostbuster: Detecting Text Ghostwritten by Large Language Models
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Vivek Verma, Eve Fleisig, Nicholas Tomlin, Dan Klein. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.
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Existing detection methods are mostly binary classifiers and cannot distinguish complex LLM-generated content such as mixed human-LLM input.
“current methods often focus on binary classification, failing to address the complexities of realworld scenarios like human-LLM collaboration.”
From Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement (2025) · cites this paper as [54]
The Additional information section says the paper’s main modeling work focuses on document-level detection and that paragraph-level detection is useful when AI assisted with only portions of an assignment. The Conclusion proposes detection within mixed human- and AI-authored documents as future work. These passages support the concern about mixed authorship, but do not establish that existing methods generally cannot distinguish it. (LLM, conf=0.83)
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- Authors
- Verma, V., Fleisig, E., Tomlin, N., & Klein, D.
- Affiliations
- University of California, Berkeley (US)
- Venue
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 1702-1717
- OpenAlex
- W4401042508
- Cited by
- 56
- References
- 0
- Citations by year
- 2026: 23 · 2025: 23 · 2024: 9 · 2023: 1
- FWCI
- 15.88
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Funders
- Open Philanthropy Project
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- large language models, authorship detection, AI-generated text, text forgery detection
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