LLM-as-Critic: Contrastive and Adversarial Strategies for Authentic Text Verification

Abstract excerpt

The rapid proliferation of sophisticated large language models (LLMs) has revolutionized content generation but concurrently poses significant challenges for distinguishing human-authored from AI-generated text. Traditional detection methods often struggle with the increasing fluency of LLM outputs and their vulnerability to adversarial manipulations. In response, we propose LLM-as-Critic, a novel discriminative f...

Topics

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• Artificial Intelligence
• Decision Support Systems, Clinical
• Electronic Health Records
• Genetic Variation
• Genetics, Medical

Identifiers and source

Literature Corpus work

b95db939-ef54-5549-930a-363beb8c5146

DOI

10.20944/preprints202506.0126.v1