Title: MMMG: A Comprehensive and Reliable Benchmark for Multitask Multimodal Generation
Primary area: datasets and benchmarks
Keywords: Evaluation, Benchmark, Multi-modal Generation Benchmark, Multimodal Large Language Models, Multimodal Generation, Image Generation, Audio Generation, Interleaved Generation

Abstract: Automatically evaluating multimodal generation presents a significant challenge, as automated metrics often struggle to align reliably with human evaluation, especially for complex tasks that involve multiple modalities. To address this, we present MMMG, a comprehensive and human-aligned benchmark for multimodal generation across 4 modality combinations (image, audio, interleaved text and image, interleaved text and audio), with a focus on tasks that present significant challenges for generation models, while still enabling reliable automatic evaluation through a combination of models and programs. MMMG encompasses 55 tasks (including 31 newly developed ones), each with a carefully designed evaluation pipeline, and 1248 instructions to systematically assess reasoning, controllability, and other key capabilities of multimodal generation models. Extensive validation demonstrates that MMMG is highly aligned with human evaluation, achieving an average agreement of 94.4%. Benchmarking results on 29 multimodal generation models reveal that even though the state-of-the-art model, GPT Image, achieves 70.7% accuracy for image generation, it falls short on multimodal reasoning and interleaved generation. Furthermore, results suggest considerable improvement space in audio generation, highlighting an important direction for future research.