Previous-Token Prediction Based LLM Near-Exact Prompt Reconstruction
Researchers have developed a new approach to inverting large language models, allowing for near-exact prompt reconstruction without requiring access to model weights or logits. This approach uses a functional method, training an explicit inverse language model from scratch on synthetically generated data from the target model. The method has been shown to outperform prior work across various evaluation metrics and exhibits transferability across different language models.
- ▪The new approach uses previous-token prediction to establish a generative link between the forward and inverse processes.
- ▪The method allows for diverse prompt reconstructions through sampling, with all reconstructed prompts inducing similar responses under the forward model.
- ▪The approach has been demonstrated to generalize across datasets and exhibits transferability in reconstructing prompts from responses generated by different language models.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2607.29378 |
| Publication time | Tue, 11 Aug 2026 20:14:15 +0000 |
| Retrieval time | 2026-08-11T20:25:45.249Z |
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Computer Science > Computation and Language arXiv:2607.29378 (cs) [Submitted on 31 Jul 2026] Title:PTP: Previous-Token Prediction based LLM Inversion for Near-Exact Prompt Reconstruction Authors:Pirzada Suhail, Nagasai Saketh Naidu, Atanu R Sinha, Amit Sethi View a PDF of the paper titled PTP: Previous-Token Prediction based LLM Inversion for Near-Exact Prompt Reconstruction, by Pirzada Suhail and 3 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) generate text by auto-regressively sampling the next token. This inherently leads to a many-to-many mapping between prompts and responses, complicating the task of inferring prompts from observed outputs. Prior work on LLM inversion frames prompt recovery as a semantic reconstruction task.
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