CoMM: A Coherent Interleaved Image-Text Dataset for Multimodal Understanding and Generation

• URL: http://arxiv.org/abs/2406.10462v1
• Date: Sat, 15 Jun 2024 01:27:58 GMT
• Title: CoMM: A Coherent Interleaved Image-Text Dataset for Multimodal Understanding and Generation
• Authors: Wei Chen, Lin Li, Yongqi Yang, Bin Wen, Fan Yang, Tingting Gao, Yu Wu, Long Chen,
• Abstract summary: We introduce CoMM, a high-quality dataset designed to enhance the coherence, consistency, and alignment of generated multimodal content. CoMM harnesses raw data from diverse sources, focusing on instructional content and visual storytelling. Various quality evaluation metrics are designed to prove the high quality of the filtered dataset.
• Score: 20.106207598099363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interleaved image-text generation has emerged as a crucial multimodal task, aiming at creating sequences of interleaved visual and textual content given a query. Despite notable advancements in recent multimodal large language models (MLLMs), generating integrated image-text sequences that exhibit narrative coherence and entity and style consistency remains challenging due to poor training data quality. To address this gap, we introduce CoMM, a high-quality Coherent interleaved image-text MultiModal dataset designed to enhance the coherence, consistency, and alignment of generated multimodal content. Initially, CoMM harnesses raw data from diverse sources, focusing on instructional content and visual storytelling, establishing a foundation for coherent and consistent content. To further refine the data quality, we devise a multi-perspective filter strategy that leverages advanced pre-trained models to ensure the development of sentences, consistency of inserted images, and semantic alignment between them. Various quality evaluation metrics are designed to prove the high quality of the filtered dataset. Meanwhile, extensive few-shot experiments on various downstream tasks demonstrate CoMM's effectiveness in significantly enhancing the in-context learning capabilities of MLLMs. Moreover, we propose four new tasks to evaluate MLLMs' interleaved generation abilities, supported by a comprehensive evaluation framework. We believe CoMM opens a new avenue for advanced MLLMs with superior multimodal in-context learning and understanding ability.

Related papers

• CUE-M: Contextual Understanding and Enhanced Search with Multimodal Large Language Model [9.451422869974557]
  This paper introduces Con Understanding and Enhanced Search with MLLM (CUE-M), a novel multimodaltextual search pipeline. Evaluations on a multimodal Q&A dataset and a public safety benchmark demonstrate that CUE-M outperforms baselines in accuracy, knowledge integration, and safety.
  arXiv  Detail & Related papers  (2024-11-19T07:16:48Z)
• MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct [148.39859547619156]
  We propose MMEvol, a novel multimodal instruction data evolution framework. MMEvol iteratively improves data quality through a refined combination of fine-grained perception, cognitive reasoning, and interaction evolution. Our approach reaches state-of-the-art (SOTA) performance in nine tasks using significantly less data compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2024-09-09T17:44:00Z)
• Fine-tuning Multimodal Large Language Models for Product Bundling [53.01642741096356]
  We introduce Bundle-MLLM, a novel framework that fine-tunes large language models (LLMs) through a hybrid item tokenization approach. Specifically, we integrate textual, media, and relational data into a unified tokenization, introducing a soft separation token to distinguish between textual and non-textual tokens. We propose a progressive optimization strategy that fine-tunes LLMs for disentangled objectives: 1) learning bundle patterns and 2) enhancing multimodal semantic understanding specific to product bundling.
  arXiv  Detail & Related papers  (2024-07-16T13:30:14Z)
• SEED-Story: Multimodal Long Story Generation with Large Language Model [66.37077224696242]
  SEED-Story is a novel method that leverages a Multimodal Large Language Model (MLLM) to generate extended multimodal stories. We propose multimodal attention sink mechanism to enable the generation of stories with up to 25 sequences (only 10 for training) in a highly efficient autoregressive manner. We present a large-scale and high-resolution dataset named StoryStream for training our model and quantitatively evaluating the task of multimodal story generation in various aspects.
  arXiv  Detail & Related papers  (2024-07-11T17:21:03Z)
• From Text to Pixel: Advancing Long-Context Understanding in MLLMs [70.78454154014989]
  We introduce SEEKER, a multimodal large language model designed to tackle this issue. SEEKER aims to optimize the compact encoding of long text by compressing the text sequence into the visual pixel space via images. Our experiments on six long-context multimodal tasks demonstrate that SEEKER can leverage fewer image tokens to convey the same amount of textual information compared with the OCR-based approach.
  arXiv  Detail & Related papers  (2024-05-23T06:17:23Z)
• Exploring the Capabilities of Large Multimodal Models on Dense Text [58.82262549456294]
  We propose the DT-VQA dataset, with 170k question-answer pairs. In this paper, we conduct a comprehensive evaluation of GPT4V, Gemini, and various open-source LMMs. We find that even with automatically labeled training datasets, significant improvements in model performance can be achieved.
  arXiv  Detail & Related papers  (2024-05-09T07:47:25Z)
• MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets [29.737965533532577]
  Multimodal Augmented Generative Images Dialogues (MAGID) is a framework to augment text-only dialogues with diverse and high-quality images. Our results show that MAGID is comparable to or better than baselines, with significant improvements in human evaluation.
  arXiv  Detail & Related papers  (2024-03-05T18:31:28Z)
• ModaVerse: Efficiently Transforming Modalities with LLMs [25.49713745405194]
  We introduce ModaVerse, a Multi-modal Large Language Model capable of comprehending and transforming content across various modalities. We propose a novel Input/Output (I/O) alignment mechanism that operates directly at the level of natural language.
  arXiv  Detail & Related papers  (2024-01-12T06:28:54Z)
• MMICT: Boosting Multi-Modal Fine-Tuning with In-Context Examples [63.78384552789171]
  This paper introduces Multi-Modal In-Context Tuning (MMICT), a novel multi-modal fine-tuning paradigm. We propose the Multi-Modal Hub (M-Hub), a unified module that captures various multi-modal features according to different inputs and objectives. Based on M-Hub, MMICT enables MM-LLMs to learn from in-context visual-guided textual features and subsequently generate outputs conditioned on the textual-guided visual features.
  arXiv  Detail & Related papers  (2023-12-11T13:11:04Z)

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.