OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation

• URL: http://arxiv.org/abs/2411.18499v3
• Date: Sun, 30 Mar 2025 07:22:46 GMT
• Title: OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
• Authors: Pengfei Zhou, Xiaopeng Peng, Jiajun Song, Chuanhao Li, Zhaopan Xu, Yue Yang, Ziyao Guo, Hao Zhang, Yuqi Lin, Yefei He, Lirui Zhao, Shuo Liu, Tianhua Li, Yuxuan Xie, Xiaojun Chang, Yu Qiao, Wenqi Shao, Kaipeng Zhang,
• Abstract summary: Multimodal Large Language Models (MLLMs) have made significant strides in visual understanding and generation tasks.<n> generating interleaved image-text content remains a challenge.<n>OpenING is a benchmark comprising 5,400 high-quality human-annotated instances across 56 real-world tasks.<n>IntJudge is a judge model for evaluating open-ended multimodal generation methods.
• Score: 59.53678957969471
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Large Language Models (MLLMs) have made significant strides in visual understanding and generation tasks. However, generating interleaved image-text content remains a challenge, which requires integrated multimodal understanding and generation abilities. While the progress in unified models offers new solutions, existing benchmarks are insufficient for evaluating these methods due to limitations in data size and diversity. To bridge this gap, we introduce OpenING, a comprehensive benchmark comprising 5,400 high-quality human-annotated instances across 56 real-world tasks. OpenING covers diverse daily scenarios such as travel guide, design, and brainstorming, offering a robust platform for challenging interleaved generation methods. In addition, we present IntJudge, a judge model for evaluating open-ended multimodal generation methods. Trained with a novel data pipeline, our IntJudge achieves an agreement rate of 82.42% with human judgments, outperforming GPT-based evaluators by 11.34%. Extensive experiments on OpenING reveal that current interleaved generation methods still have substantial room for improvement. Key findings on interleaved image-text generation are further presented to guide the development of next-generation models.

Related papers

• Benchmarking Retrieval-Augmented Multimomal Generation for Document Question Answering [42.468210353582755]
  Document Visual Question Answering (DocVQA) faces dual challenges in processing lengthy multimodal documents.<n>Current document retrieval-augmented generation (DocRAG) methods remain limited by their text-centric approaches.<n>We introduce MMDocRAG, a comprehensive benchmark featuring 4,055 expert-annotated QA pairs with multi-page, cross-modal evidence chains.
  arXiv  Detail & Related papers  (2025-05-22T09:52:57Z)
• MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models [71.36392373876505]
  We introduce MMIE, a large-scale benchmark for evaluating interleaved multimodal comprehension and generation in Large Vision-Language Models (LVLMs) MMIE comprises 20K meticulously curated multimodal queries, spanning 3 categories, 12 fields, and 102 subfields, including mathematics, coding, physics, literature, health, and arts. It supports both interleaved inputs and outputs, offering a mix of multiple-choice and open-ended question formats to evaluate diverse competencies.
  arXiv  Detail & Related papers  (2024-10-14T04:15:00Z)
• Harmonizing Visual Text Comprehension and Generation [31.605599298507293]
  We present TextHarmony, a unified and versatile multimodal generative model proficient in comprehending and generating visual text. We propose Slide-LoRA, which aggregates modality-specific and modality-agnostic LoRA experts, partially decoupling the multimodal generation space. Comprehensive experiments across various benchmarks demonstrate the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2024-07-23T10:11:56Z)
• Retrieval is Accurate Generation [99.24267226311157]
  We introduce a novel method that selects context-aware phrases from a collection of supporting documents. Our model achieves the best performance and the lowest latency among several retrieval-augmented baselines.
  arXiv  Detail & Related papers  (2024-02-27T14:16:19Z)
• Exploring Precision and Recall to assess the quality and diversity of LLMs [82.21278402856079]
  We introduce a novel evaluation framework for Large Language Models (LLMs) such as textscLlama-2 and textscMistral. This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora.
  arXiv  Detail & Related papers  (2024-02-16T13:53:26Z)
• MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models [70.92847554971065]
  We introduce MT-Eval, a comprehensive benchmark designed to evaluate multi-turn conversational abilities. By analyzing human-LLM conversations, we categorize interaction patterns into four types: recollection, expansion, refinement, and follow-up. Our evaluation of 11 well-known LLMs shows that while closed-source models generally surpass open-source ones, certain open-source models exceed GPT-3.5-Turbo in specific tasks.
  arXiv  Detail & Related papers  (2024-01-30T04:50:28Z)
• MiniGPT-5: Interleaved Vision-and-Language Generation via Generative Vokens [22.802963850131306]
  We introduce a novel interleaved vision-and-language generation method, centered around the concept of generative vokens. Our method is marked by a unique two-stage training strategy for description-free multimodal generation. Our model, MiniGPT-5, exhibits substantial improvement over the baseline models on multimodal generation datasets.
  arXiv  Detail & Related papers  (2023-10-03T17:49:04Z)
• L-Eval: Instituting Standardized Evaluation for Long Context Language Models [91.05820785008527]
  We propose L-Eval to institute a more standardized evaluation for long context language models (LCLMs) We build a new evaluation suite containing 20 sub-tasks, 508 long documents, and over 2,000 human-labeled query-response pairs. Results show that popular n-gram matching metrics generally can not correlate well with human judgment.
  arXiv  Detail & Related papers  (2023-07-20T17:59:41Z)
• JourneyDB: A Benchmark for Generative Image Understanding [89.02046606392382]
  We introduce a comprehensive dataset, referred to as JourneyDB, that caters to the domain of generative images. Our meticulously curated dataset comprises 4 million distinct and high-quality generated images. On our dataset, we have devised four benchmarks to assess the performance of generated image comprehension.
  arXiv  Detail & Related papers  (2023-07-03T02:39:08Z)
• MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text [58.655375327681774]
  We propose the first Multimodal Retrieval-Augmented Transformer (MuRAG) MuRAG accesses an external non-parametric multimodal memory to augment language generation. Our results show that MuRAG achieves state-of-the-art accuracy, outperforming existing models by 10-20% absolute on both datasets.
  arXiv  Detail & Related papers  (2022-10-06T13:58:03Z)
• Mutual Information Divergence: A Unified Metric for Multimodal Generative Models [19.520177195241704]
  We propose the negative Gaussian cross-mutual information using the CLIP features as a unified metric, coined by Mutual Information Divergence (MID) We extensively compare it with competing metrics using carefully-generated or human-annotated judgments in text-to-image generation and image captioning tasks. The proposed MID significantly outperforms the competitive methods by having consistency across benchmarks, sample parsimony, and robustness toward the exploited CLIP model.
  arXiv  Detail & Related papers  (2022-05-25T09:34:37Z)

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.