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RECAP: Reproducing Copyrighted Data from LLMs Training with an Agentic Pipeline

André V. Duarte, Xuying li, Bin Zeng, Arlindo L. Oliveira, Lei Li, Zhuo Li · Oct 29, 2025 · Citations: 0

Automatic Metrics General Red Team
Open arXiv Find Implementation RSS feed Shortlist (0)

How to use this page

Moderate trust

Use this for comparison and orientation, not as your only source.

Best use

Secondary protocol comparison source

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

Moderate

Derived from extracted protocol signals and abstract evidence.

Abstract

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.

Should You Rely On This Paper?

This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.

Best use

Secondary protocol comparison source

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

No major weakness surfaced.

Trust level

Moderate

Usefulness score

65/100 • Medium

Useful as a secondary reference; validate protocol details against neighboring papers.

Human Feedback Signal

Detected

Evaluation Signal

Detected

Usefulness for eval research

Moderate-confidence candidate

Extraction confidence 70%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

strong

Red Team

Directly usable for protocol triage.

"If we cannot inspect the training data of a large language model (LLM), how can we ever know what it has seen?"

Evaluation Modes

strong

Automatic Metrics

Includes extracted eval setup.

"If we cannot inspect the training data of a large language model (LLM), how can we ever know what it has seen?"

Quality Controls

missing

Not reported

No explicit QC controls found.

"If we cannot inspect the training data of a large language model (LLM), how can we ever know what it has seen?"

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"If we cannot inspect the training data of a large language model (LLM), how can we ever know what it has seen?"

Reported Metrics

strong

Rouge

Useful for evaluation criteria comparison.

"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."

Human Feedback Details

  • Uses human feedback: Yes
  • Feedback types: Red Team
  • Rater population: Not reported
  • Expertise required: General

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Moderate
  • Use this page as: Secondary protocol comparison source

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

rouge

Research Brief

Metadata summary

If we cannot inspect the training data of a large language model (LLM), how can we ever know what it has seen?

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • 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.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Research Summary

Contribution Summary

  • As such, we propose RECAP, an agentic pipeline designed to elicit and verify memorized training data from LLM outputs.
  • 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.

Why It Matters For Eval

  • As such, we propose RECAP, an agentic pipeline designed to elicit and verify memorized training data from LLM outputs.
  • 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.

Researcher Checklist

  • Pass: Human feedback protocol is explicit

    Detected: Red Team

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Pass: Metric reporting is present

    Detected: rouge

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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