REVIEW 5 cited by
Ghostbuster: Detecting Text Ghostwritten by Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
We introduce Ghostbuster, a state-of-the-art system for detecting AI-generated text. Our method works by passing documents through a series of weaker language models, running a structured search over possible combinations of their features, and then training a classifier on the selected features to predict whether documents are AI-generated. Crucially, Ghostbuster does not require access to token probabilities from the target model, making it useful for detecting text generated by black-box models or unknown model versions. In conjunction with our model, we release three new datasets of human- and AI-generated text as detection benchmarks in the domains of student essays, creative writing, and news articles. We compare Ghostbuster to a variety of existing detectors, including DetectGPT and GPTZero, as well as a new RoBERTa baseline. Ghostbuster achieves 99.0 F1 when evaluated across domains, which is 5.9 F1 higher than the best preexisting model. It also outperforms all previous approaches in generalization across writing domains (+7.5 F1), prompting strategies (+2.1 F1), and language models (+4.4 F1). We also analyze the robustness of our system to a variety of perturbations and paraphrasing attacks and evaluate its performance on documents written by non-native English speakers.
Forward citations
Cited by 5 Pith papers
-
Detecting LLM-generated Code with Subtle Modification by Adversarial Training
CodeGPTSensor+, trained with adversarial samples that combine identifier renaming and structure transformation, is substantially more robust to subtle modifications of LLM-generated code than the original CodeGPTSensor.
-
DAMAGE: Detecting Adversarially Modified AI Generated Text
Adding humanizer-processed text to training data yields a detector that catches 98.26% of humanized AI essays at a 5% false-positive rate and stays robust to a detector-targeted attack.
-
Who Gets Seen in the Age of AI? Adoption Patterns of Large Language Models in Scholarly Writing and Citation Outcomes
Analyzing 98,000 Scopus computer science papers, the paper finds a global rise in AI-like writing after ChatGPT and reports regional differences in citation returns, but the key differential-gain result is statistical...
-
GenAI Content Detection Task 1: English and Multilingual Machine-Generated Text Detection: AI vs. Human
A COLING 2025 shared task benchmark showing that current machine-generated text detectors reach only moderate accuracy and degrade badly on out-of-domain and humanized AI text.
-
Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework
LM2OTIFS uses word co-occurrence graphs and GNNExplainer to detect and explain machine-generated text, with strong in-domain accuracy but unsupported faithfulness claims and a flawed theoretical proof.
Discussion (0). Continue with ORCID to comment.