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Edge 360: Meet Ghostbuster: An AI Technique for Detecting LLM-Generated Content

Edge 360: Meet Ghostbuster: An AI Technique for Detecting LLM-Generated Content

Created by Berkeley University, the new method uses a probability distribution method to detect the likelihood of AI-generated tokens within a document.

Jan 11, 2024
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Edge 360: Meet Ghostbuster: An AI Technique for Detecting LLM-Generated Content
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A futuristic, abstract interpretation of an AI language model named 'Ghostbuster', designed for detecting fake content. This AI is depicted as a dynamic, ethereal entity in a high-tech environment. It has a ghost-like appearance, but with a digital twist, featuring glowing circuits and holographic elements. The AI hovers above a vast array of digital documents, represented as floating, glowing pages. These pages are in a state of constant motion, symbolizing the AI's analysis. The scene is illuminated by neon lights, with an overall ambiance that blends the supernatural with advanced technology. The AI is actively scanning the documents, symbolized by beams of light scanning across the pages, looking for signs of fake content.
Created Using DALL-E

The rapid evolution of large language models(LLMs) has created new challenges in terms of differentiating between human and AI-generated content. Recently, we have seen all sorts of solutions emerge to try to tackle this challenge, but the number of false positives is quite concerning. Recently, Berkeley AI Research(BAIR) published a new paper introducing a technique for identifying AI-generated content.

Ghostbuster, as presented in a recent research paper, emerges as a formidable solution for the identification of AI-generated text. Its operational framework revolves around the meticulous calculation of the likelihood of generating each token within a document under the scrutiny of various weaker language models. Subsequently, Ghostbuster employs a fusion of functions derived from these token probabilities to serve as inputs for a conclusive classifier.

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