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 method of language model inversion is a new technique that builds on previous work in inverting deep embeddings in computer vision. It aims to address privacy concerns in text embeddings from encoder models by recovering hidden prompts from language model outputs. This approach is unique and related to prior research on model inversion, membership inference, and model stealing in NLP models. The study emphasizes prompt recovery as a way to tackle privacy concerns.
The research addresses language model inversion, aiming to recover input prompts from a model’s next-token probabilities, crucial in scenarios where users lack access to the original prompt. They emphasize the potential invertibility of language model predictions, showcasing the recovery of similar or exact prompts. The study explores various access patterns, including text-only access, demonstrating prompt recovery feasibility with limited information.
The study introduces a method for recovering unknown prompts from a language model’s distribution output. It employs a conditional language model trained on a Transformer-based model, mapping next-token probabilities to tokens. Cross-attention in an encoder-decoder Transformer is utilized, unrolling the vector into pseudo-embeddings. Experiments on the Llama-2 7b dataset demonstrate qualitative examples of inverted prompts. They establish baselines, including jailbreak strings, for method performance comparison.
The proposed inversion method in the study excels in recovering prompts from the Instructions-2M test set, surpassing few-shot prompting and even outperforming GPT-4. It demonstrates success across various model access scenarios, achieving notable BLEU scores and token-level F1 on the Llama-2 7b dataset. Transferability to models of different sizes is explored, showing good performance in code generation tasks. Qualitative analysis reveals on-topic and syntactically similar reconstructed prompts, indicating the inversion method’s efficacy in accurately recovering prompts from language model outputs.
In conclusion, the study has shown that language model inversion is a reliable method for recovering prompts from a model’s output distribution. To protect against inversion attacks, it is important to implement defense mechanisms such as adding noise and setting restricted access. The experiments have demonstrated that model probability distributions can be reconstructed with enabled sampling. Still, limiting the top-logits access and setting the temperature to 0 for prompt protection is recommended. The results confirm that language model inversion is an effective method for accurately recovering hidden prompts from language models.
Future work in language model inversion could delve into inputting single suffixes to generate multiple next-token predictions, not just at the end. Research may focus on assessing the transferability of inversions across models of different sizes and domains. Investigating the impact of various defense mechanisms, including noise addition and top-logits access restrictions, presents a valuable avenue for exploration. Parameterizations integrating token embeddings with probability values could enhance inversion model performance. Exploring the method’s application to diverse tasks, like code generation, would offer insights into its broader utility. Further analysis is needed to understand the limitations and challenges in prompt recovery, especially in handling proper nouns and improving syntactic similarity.
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The post Cornell Researchers Uncover Insights into Language Model Prompts: A Deep Dive into How Next-Token Probabilities Can Reveal Hidden Text appeared first on MarkTechPost.
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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