Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models

• URL: http://arxiv.org/abs/2503.12072v1
• Date: Sat, 15 Mar 2025 10:19:15 GMT
• Title: Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models
• Authors: Abhilasha Ravichander, Jillian Fisher, Taylor Sorensen, Ximing Lu, Yuchen Lin, Maria Antoniak, Niloofar Mireshghallah, Chandra Bhagavatula, Yejin Choi,
• Abstract summary: We show how to identify training data known to proprietary large language models (LLMs) by using information-guided probes.<n>Our work builds on a key observation: text passages with high surprisal are good search material for memorization probes.
• Score: 52.439289085318634
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-quality training data has proven crucial for developing performant large language models (LLMs). However, commercial LLM providers disclose few, if any, details about the data used for training. This lack of transparency creates multiple challenges: it limits external oversight and inspection of LLMs for issues such as copyright infringement, it undermines the agency of data authors, and it hinders scientific research on critical issues such as data contamination and data selection. How can we recover what training data is known to LLMs? In this work, we demonstrate a new method to identify training data known to proprietary LLMs like GPT-4 without requiring any access to model weights or token probabilities, by using information-guided probes. Our work builds on a key observation: text passages with high surprisal are good search material for memorization probes. By evaluating a model's ability to successfully reconstruct high-surprisal tokens in text, we can identify a surprising number of texts memorized by LLMs.

Related papers

• Towards Robust Evaluation of Unlearning in LLMs via Data Transformations [17.927224387698903]
  Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents. In recent times research in the area of Machine Unlearning (MUL) has become active. Main idea is to force LLMs to forget (unlearn) certain information (e.g., PII) without suffering from performance loss on regular tasks.
  arXiv  Detail & Related papers  (2024-11-23T07:20:36Z)
• Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data [54.934578742209716]
  In real-world NLP applications, Large Language Models (LLMs) offer promising solutions due to their extensive training on vast datasets.<n>LLKD is an adaptive sample selection method that incorporates signals from both the teacher and student.<n>Our comprehensive experiments show that LLKD achieves superior performance across various datasets with higher data efficiency.
  arXiv  Detail & Related papers  (2024-11-12T18:57:59Z)
• Formality is Favored: Unraveling the Learning Preferences of Large Language Models on Data with Conflicting Knowledge [55.65162959527848]
  Large language models have shown excellent performance on many knowledge-intensive tasks. However, pretraining data tends to contain misleading and even conflicting information. This study systematically analyze LLMs' learning preferences for data with conflicting knowledge.
  arXiv  Detail & Related papers  (2024-10-07T06:49:41Z)
• LLM-PBE: Assessing Data Privacy in Large Language Models [111.58198436835036]
  Large Language Models (LLMs) have become integral to numerous domains, significantly advancing applications in data management, mining, and analysis. Despite the critical nature of this issue, there has been no existing literature to offer a comprehensive assessment of data privacy risks in LLMs. Our paper introduces LLM-PBE, a toolkit crafted specifically for the systematic evaluation of data privacy risks in LLMs.
  arXiv  Detail & Related papers  (2024-08-23T01:37:29Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• Special Characters Attack: Toward Scalable Training Data Extraction From Large Language Models [36.58320580210008]
  We show that certain special characters or their combinations with English letters are stronger memory triggers, leading to more severe data leakage. We propose a simple but effective Special Characters Attack (SCA) to induce training data leakage.
  arXiv  Detail & Related papers  (2024-05-09T02:35:32Z)
• Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models [21.10890310571397]
  Large Language Models (LLMs) can be applied to a diverse set of tasks, but the critical issues of data contamination and memorization are often glossed over.<n>This work introduces a variety of different techniques to assess whether a language model has seen a dataset during training.<n>We then compare the few-shot learning performance of LLMs on datasets that were seen during training to the performance on datasets released after training.
  arXiv  Detail & Related papers  (2024-04-09T10:58:21Z)
• Elephants Never Forget: Testing Language Models for Memorization of Tabular Data [21.912611415307644]
  Large Language Models (LLMs) can be applied to a diverse set of tasks, but the critical issues of data contamination and memorization are often glossed over. We introduce a variety of different techniques to assess the degrees of contamination, including statistical tests for conditional distribution modeling and four tests that identify memorization.
  arXiv  Detail & Related papers  (2024-03-11T12:07:13Z)
• Large Language Models for Data Annotation and Synthesis: A Survey [49.8318827245266]
  This survey focuses on the utility of Large Language Models for data annotation and synthesis.<n>It includes an in-depth taxonomy of data types that LLMs can annotate, a review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation and synthesis.
  arXiv  Detail & Related papers  (2024-02-21T00:44:04Z)
• LLMaAA: Making Large Language Models as Active Annotators [32.57011151031332]
  We propose LLMaAA, which takes large language models as annotators and puts them into an active learning loop to determine what to annotate efficiently. We conduct experiments and analysis on two classic NLP tasks, named entity recognition and relation extraction. With LLMaAA, task-specific models trained from LLM-generated labels can outperform the teacher within only hundreds of annotated examples.
  arXiv  Detail & Related papers  (2023-10-30T14:54:15Z)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)
• CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages [86.90220551111096]
  Training datasets for large language models (LLMs) are often not fully disclosed. We present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages.
  arXiv  Detail & Related papers  (2023-09-17T23:49:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.