Papers by Vivek Verma

2 papers
Ghostbuster: Detecting Text Ghostwritten by Large Language Models (2024.naacl-long)

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Challenge: Ghostbuster is a system that passes documents through weaker language models, runs a structured search over possible combinations of their features, and trains a classifier on the selected features.
Approach: They propose a method that passes documents through weaker language models, runs a structured search over possible combinations of their features, and trains a classifier on the selected features.
Outcome: The proposed method outperforms existing detectors and a new baseline on student essays, creative writing, and news articles.
Revisiting Entropy Rate Constancy in Text (2023.findings-emnlp)

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Challenge: Existing evidence supports the uniform information density hypothesis . however, we re-evaluate the hypothesis with neural language models .
Approach: They propose to use n-gram language models to argue that English documents exhibit entropy rate constancy . they re-evaluate the claims of Genzel and Charniak with neural language models .
Outcome: The proposed hypothesis fails to support the proposed hypothesis with language models.

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