Cornell Researchers Uncover Insights into Language Model Prompts: A Deep Dive into How Next-Token Probabilities Can Reveal Hidden Text

The study conducted by researchers at Cornell University addresses the problem of language model inversion. They discovered that the next-token probabilities contain significant information about the preceding text. To solve this problem, they introduced a method to reconstruct unknown prompts using only the model’s current distribution output, which they found to be highly accurate. The […]

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