Train a Unified Multimodal Data Quality Classifier with Synthetic Data

• URL: http://arxiv.org/abs/2510.15162v1
• Date: Thu, 16 Oct 2025 21:53:28 GMT
• Title: Train a Unified Multimodal Data Quality Classifier with Synthetic Data
• Authors: Weizhi Wang, Rongmei Lin, Shiyang Li, Colin Lockard, Ritesh Sarkhel, Sanket Lokegaonkar, Jingbo Shang, Xifeng Yan, Nasser Zalmout, Xian Li,
• Abstract summary: Multimodal Large Language Models (MLLMs) are continually pre-trained on a mixture of image-text caption data and interleaved document data.<n>We propose to train an efficient MLLM as a Unified Mulitmodal Data Quality to Filter both high-quality image-text caption and interleaved data.
• Score: 56.872668770081766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Multimodal Large Language Models (MLLMs) are continually pre-trained on a mixture of image-text caption data and interleaved document data, while the high-quality data filtering towards image-text interleaved document data is under-explored. We propose to train an efficient MLLM as a Unified Mulitmodal Data Quality Classifier to Filter both high-quality image-text caption and interleaved data (UniFilter). To address the challenge of collecting diverse labeled multimodal data, we introduce a semi-synthetic approach that leverages readily available raw images and generates corresponding text across four quality levels. This method enables efficient creation of sample-score pairs for both caption and interleaved document data to train UniFilter. We apply UniFilter to curate high-quality caption data from DataComp caption dataset and interleaved data from the OBELICS image-text interleaved dataset. MLLMs pre-trained on the filtered data demonstrate significantly enhanced capabilities compared to those trained on baseline-filtered data, achieving stronger zero-shot reasoning and in-context learning capabilities. After visual supervised fine-tuning, these UniFilter-induced MLLMs achieve stronger performance on various benchmarks, highlighting the downstream benefits of high-quality multimodal pre-training. We release the synthetic training data used for training UniFilter, the UniFilter model checkpoints, and the high-quality interleaved document subset OBELICS-HQ, curated by UniFilter, to the community for reproduction and further development.

Related papers

• HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models [15.877790469608662]
  We introduce an LVLM-driven data refinement pipeline to enhance the quality of image-text pair data.<n>We propose a training paradigm that extends conventional contrastive learning by incorporating negative descriptions and short tags.<n>Our approach achieves state-of-the-art performance in zero-shot classification, cross-modal retrieval, and fine-grained visual understanding tasks.
  arXiv  Detail & Related papers  (2025-07-30T07:21:36Z)
• Multimodal LLMs as Customized Reward Models for Text-to-Image Generation [60.164968941945645]
  We introduce LLaVA-Reward, an efficient reward model designed to automatically evaluate text-to-image (T2I) generations across multiple perspectives.<n>LLaVA-Reward directly utilizes the hidden states of multimodal large language models (MLLMs)<n>We train LLaVA-Reward on four evaluation perspectives: text-image alignment, fidelity/artifact, safety, and overall ranking.
  arXiv  Detail & Related papers  (2025-07-28T23:52:53Z)
• Trust the Model: Compact VLMs as In-Context Judges for Image-Text Data Quality [5.750869893508341]
  Vision-language models (VLMs) extend the conventional large language models by integrating visual data, enabling richer multimodal reasoning.<n>We introduce a streamlined data filtration framework that employs a compact VLM, fine-tuned on a high-quality image-caption annotated dataset.<n>This model effectively evaluates and filters potential training samples based on caption and image quality and alignment.
  arXiv  Detail & Related papers  (2025-07-27T07:20:25Z)
• Towards Visual Text Grounding of Multimodal Large Language Model [74.22413337117617]
  We introduce TRIG, a novel task with a newly designed instruction dataset for benchmarking text-rich image grounding.<n>Specifically, we propose an OCR-LLM-human interaction pipeline to create 800 manually annotated question-answer pairs as a benchmark.<n>A comprehensive evaluation of various MLLMs on our proposed benchmark exposes substantial limitations in their grounding capability on text-rich images.
  arXiv  Detail & Related papers  (2025-04-07T12:01:59Z)
• Few-shot LLM Synthetic Data with Distribution Matching [37.55363714371521]
  Large language models (LLMs) produce high-quality synthetic data to enhance the performance of smaller models.<n>LLMs-generated synthetic data often differs from the real data in key language attributes.<n>We introduce SynAlign: a synthetic data generation and filtering framework based on key attribute distribution matching.
  arXiv  Detail & Related papers  (2025-02-09T16:43:32Z)
• Beyond Filtering: Adaptive Image-Text Quality Enhancement for MLLM Pretraining [31.176432567292093]
  We propose the Adaptive Image-Text Quality Enhancer (AITQE), a model that dynamically assesses and enhances the quality of image-text pairs. AITQE employs a text rewriting mechanism for low-quality pairs and incorporates a negative sample learning strategy to improve evaluative capabilities.
  arXiv  Detail & Related papers  (2024-10-21T16:32:41Z)
• ScalingFilter: Assessing Data Quality through Inverse Utilization of Scaling Laws [67.59263833387536]
  ScalingFilter is a novel approach that evaluates text quality based on the perplexity difference between two language models trained on the same data. To assess the bias introduced by quality filtering, we introduce semantic diversity, a metric of utilizing text embedding models for semantic representations.
  arXiv  Detail & Related papers  (2024-08-15T17:59:30Z)
• Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters [38.41887207958015]
  We propose a novel framework for filtering image-text data by leveraging fine-tuned Multimodal Language Models (MLMs) Our filter can generalize to different models and tasks, and be used as a drop-in replacement for CLIPScore.
  arXiv  Detail & Related papers  (2024-03-05T06:05:15Z)
• Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection [59.11430077029321]
  We introduce a novel dataset selection method, Self-Filter, for vision-language models (VLMs) In the first stage, we devise a scoring network to evaluate the difficulty of training instructions, which is co-trained with the VLM. In the second stage, we use the trained score net to measure the difficulty of each instruction, select the most challenging samples, and penalize similar samples to encourage diversity.
  arXiv  Detail & Related papers  (2024-02-19T20:08:48Z)
• Sieve: Multimodal Dataset Pruning Using Image Captioning Models [11.362835828985494]
  Vision-Language Models (VLMs) are pretrained on large, diverse, and noisy web-crawled datasets. We argue that this approach suffers from multiple limitations including false positives and negatives due to CLIP's pretraining on noisy labels. We propose a pruning signal, Sieve, that employs synthetic captions generated by image-captioning models pretrained on small, diverse, and well-aligned image-text pairs.
  arXiv  Detail & Related papers  (2023-10-03T14:53:53Z)
• MLLM-DataEngine: An Iterative Refinement Approach for MLLM [62.30753425449056]
  We propose a novel closed-loop system that bridges data generation, model training, and evaluation. Within each loop, the MLLM-DataEngine first analyze the weakness of the model based on the evaluation results. For targeting, we propose an Adaptive Bad-case Sampling module, which adjusts the ratio of different types of data. For quality, we resort to GPT-4 to generate high-quality data with each given data type.
  arXiv  Detail & Related papers  (2023-08-25T01:41:04Z)

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.