Text und Bilder wurden geleakt! Eine systematische Analyse von multimodaler LLM-Datenkontamination.

Both Text and Images Leaked! A Systematic Analysis of Multimodal LLM Data Contamination

November 6, 2024
Autoren: Dingjie Song, Sicheng Lai, Shunian Chen, Lichao Sun, Benyou Wang
cs.AI

Zusammenfassung

Die rasante Entwicklung von multimodalen großen Sprachmodellen (MLLMs) hat eine überlegene Leistung bei verschiedenen multimodalen Benchmarks gezeigt. Allerdings führt das Problem der Datenkontamination während des Trainings zu Herausforderungen bei der Leistungsbewertung und -vergleich. Obwohl zahlreiche Methoden zur Erkennung von Datensatzkontamination in großen Sprachmodellen (LLMs) existieren, sind sie für MLLMs aufgrund ihrer verschiedenen Modalitäten und mehreren Trainingsphasen weniger effektiv. In dieser Studie stellen wir ein multimodales Rahmenwerk zur Erkennung von Datenkontamination, MM-Detect, vor, das speziell für MLLMs entwickelt wurde. Unsere experimentellen Ergebnisse zeigen, dass MM-Detect empfindlich auf unterschiedliche Grade von Kontamination reagiert und signifikante Leistungsverbesserungen aufgrund von Datenlecks des Trainingssets multimodaler Benchmarks aufzeigen kann. Darüber hinaus untersuchen wir auch die Möglichkeit der Kontamination, die aus der Vorphase des Trainings von LLMs, die von MLLMs verwendet werden, und der Feinabstimmungsphase von MLLMs stammt, und bieten neue Einblicke in die Phasen, in denen Kontamination eingeführt werden kann.
English
The rapid progression of multimodal large language models (MLLMs) has demonstrated superior performance on various multimodal benchmarks. However, the issue of data contamination during training creates challenges in performance evaluation and comparison. While numerous methods exist for detecting dataset contamination in large language models (LLMs), they are less effective for MLLMs due to their various modalities and multiple training phases. In this study, we introduce a multimodal data contamination detection framework, MM-Detect, designed for MLLMs. Our experimental results indicate that MM-Detect is sensitive to varying degrees of contamination and can highlight significant performance improvements due to leakage of the training set of multimodal benchmarks. Furthermore, We also explore the possibility of contamination originating from the pre-training phase of LLMs used by MLLMs and the fine-tuning phase of MLLMs, offering new insights into the stages at which contamination may be introduced.

Summary

AI-Generated Summary

Paper Overview

The study investigates detecting data contamination in multimodal large language models (MLLMs) during training, introducing the MM-Detect framework to address challenges in existing methods. It explores contamination from both pre-training LLMs and fine-tuning MLLMs, highlighting the impact on model performance and the significance of detecting and mitigating such contamination.

Core Contribution

  • Introduces the MM-Detect framework tailored for MLLMs to identify varying degrees of contamination.
  • Explores contamination from pre-training LLMs and fine-tuning MLLMs, offering insights into when contamination occurs.
  • Defines multimodal contamination detection, presenting a novel approach to detect and quantify contamination levels.
  • Provides specific methods like Option Order Sensitivity Test and Slot Guessing for Perturbation Captions within the MM-Detect framework.

Research Context

  • Addresses the limitations of existing contamination detection methods in MLLMs due to their multimodal nature and multi-stage training.
  • Evaluates contamination in open-source and proprietary MLLMs across various datasets to assess performance impacts.
  • Highlights the importance of addressing contamination to ensure model performance consistency and generalization ability.

Keywords

Multimodal Large Language Models (MLLMs), Data Contamination Detection, MM-Detect Framework, Pre-training, Fine-tuning, Benchmark Datasets, Leakage Detection, Cross-modal Contamination

Background

The research focuses on detecting data contamination in MLLMs, emphasizing the challenges posed by their multimodal nature and multi-stage training. Existing methods lack effectiveness in detecting contamination in MLLMs, necessitating the development of a specialized framework like MM-Detect.

Research Gap

  • Limited effectiveness of current contamination detection methods in MLLMs due to their unique characteristics.
  • Lack of specific frameworks for identifying and quantifying contamination levels in multimodal models.
  • Insufficient understanding of the impact of contamination on MLLM performance and generalization.

Technical Challenges

  • Inefficiency of unimodal methods in detecting contamination in multimodal datasets.
  • Complexities arising from the multi-stage training process of MLLMs.
  • Need for precise detection metrics to quantify contamination levels accurately.

Prior Approaches

  • Existing methods like Logits-based, Masking-based, and Comparison-based techniques for contamination detection.
  • Challenges in applying traditional unimodal contamination detection methods to MLLMs.
  • Limited exploration of contamination originating from both pre-training LLMs and fine-tuning MLLMs.

Methodology

The research methodology involves developing the MM-Detect framework to detect and quantify contamination in MLLMs, focusing on both pre-training and fine-tuning stages.

Theoretical Foundation

  • Utilizes a theoretical basis to define and quantify multimodal contamination in MLLMs.
  • Incorporates mathematical models to assess contamination levels and performance impacts.

Technical Architecture

  • MM-Detect framework comprises specific methods like Option Order Sensitivity Test and Slot Guessing for Perturbation Captions.
  • Involves a structured detection pipeline algorithm to calculate atomic metrics accurately.

Implementation Details

  • Utilizes the MM-Detect framework to evaluate contamination in MLLMs across multiple datasets.
  • Implements specific algorithms to detect leakage from benchmark datasets and assess performance improvements.

Innovation Points

  • Introduces a specialized framework, MM-Detect, tailored for detecting contamination in MLLMs.
  • Provides novel methods for quantifying contamination levels in multimodal models.
  • Explores the stages at which contamination may be introduced in MLLMs.

Experimental Validation

The experimental validation assesses the effectiveness of MM-Detect in identifying contamination in MLLMs and its impact on model performance.

Setup

  • Evaluation conducted on open-source and proprietary MLLMs using datasets like ScienceQA, MMStar, COCO-Caption2017, NoCaps, and Vintage.
  • Configurations include specific parameters to detect and quantify contamination levels accurately.

Metrics

  • Detection metrics involve calculating benchmark atomic metrics and analyzing contamination degrees at dataset and instance levels.
  • Quantitative evaluation criteria used to measure the effectiveness of MM-Detect in identifying varying degrees of contamination.

Results

  • Experimental results demonstrate the ability of MM-Detect to identify contamination and its impact on model performance.
  • Showcases the significance of detecting and mitigating contamination to ensure model consistency and generalization.

Comparative Analysis

  • Compares the performance of MLLMs with and without contamination detection using MM-Detect.
  • Highlights the advantages of detecting and addressing contamination in improving model performance and reliability.

Impact and Implications

The study's findings have significant implications for the field of multimodal language models, emphasizing the importance of addressing contamination for model performance and evaluation consistency.

Key Findings

  • MM-Detect effectively identifies varying degrees of contamination in MLLMs.
  • Leakage from benchmark datasets can significantly enhance model performance, leading to evaluation bias.
  • Cross-modal contamination between MLLMs and benchmark datasets impacts model generalization.

Limitations

  • Challenges in detecting test set contamination and standardizing multimodal dataset use.
  • Need for ongoing evaluation and benchmarking systems to address contamination issues effectively.

Future Directions

  • Standardizing multimodal dataset usage and contamination detection methodologies.
  • Addressing limitations in detecting and mitigating contamination in MLLMs.
  • Exploring practical applications and real-world implications of contamination detection frameworks.

Practical Significance

  • Ensuring data consistency and model performance reliability in MLLMs.
  • Enhancing model generalization and evaluation accuracy through contamination detection.
  • Facilitating the development of robust and trustworthy multimodal language models.

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