RECAP: Reproducing Copyrighted Data from LLMs Training with an Agentic Pipeline
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arXiv
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| Format: | Preprint |
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2025
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| author | Duarte, André V. li, Xuying Zeng, Bin Oliveira, Arlindo L. Li, Lei Li, Zhuo |
| author_facet | Duarte, André V. li, Xuying Zeng, Bin Oliveira, Arlindo L. Li, Lei Li, Zhuo |
| contents | If we cannot inspect the training data of a large language model (LLM), how can we ever know what it has seen? We believe the most compelling evidence arises when the model itself freely reproduces the target content. As such, we propose RECAP, an agentic pipeline designed to elicit and verify memorized training data from LLM outputs. At the heart of RECAP is a feedback-driven loop, where an initial extraction attempt is evaluated by a secondary language model, which compares the output against a reference passage and identifies discrepancies. These are then translated into minimal correction hints, which are fed back into the target model to guide subsequent generations. In addition, to address alignment-induced refusals, RECAP includes a jailbreaking module that detects and overcomes such barriers. We evaluate RECAP on EchoTrace, a new benchmark spanning over 30 full books, and the results show that RECAP leads to substantial gains over single-iteration approaches. For instance, with GPT-4.1, the average ROUGE-L score for the copyrighted text extraction improved from 0.38 to 0.47 - a nearly 24% increase. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RECAP: Reproducing Copyrighted Data from LLMs Training with an Agentic Pipeline Duarte, André V. li, Xuying Zeng, Bin Oliveira, Arlindo L. Li, Lei Li, Zhuo Computation and Language I.2 If we cannot inspect the training data of a large language model (LLM), how can we ever know what it has seen? We believe the most compelling evidence arises when the model itself freely reproduces the target content. As such, we propose RECAP, an agentic pipeline designed to elicit and verify memorized training data from LLM outputs. At the heart of RECAP is a feedback-driven loop, where an initial extraction attempt is evaluated by a secondary language model, which compares the output against a reference passage and identifies discrepancies. These are then translated into minimal correction hints, which are fed back into the target model to guide subsequent generations. In addition, to address alignment-induced refusals, RECAP includes a jailbreaking module that detects and overcomes such barriers. We evaluate RECAP on EchoTrace, a new benchmark spanning over 30 full books, and the results show that RECAP leads to substantial gains over single-iteration approaches. For instance, with GPT-4.1, the average ROUGE-L score for the copyrighted text extraction improved from 0.38 to 0.47 - a nearly 24% increase. |
| title | RECAP: Reproducing Copyrighted Data from LLMs Training with an Agentic Pipeline |
| topic | Computation and Language I.2 |
| url | https://arxiv.org/abs/2510.25941 |