IMUG-Bench: Benchmarking Unified Multimodal Models on Interleaved Understanding and Generation
IMUG-Bench evaluates UMMs on multi-turn image-text dialogues, revealing exposure bias in generation.
Key Findings
Methodology
IMUG-Bench evaluates UMMs' understanding and generation capabilities through multi-turn interactions, covering Static Spatial, Temporal Causal, and Hybrid tasks. Samples are generated using LLM and VLM template filling, with two-person verification ensuring quality.
Key Results
- Experiments reveal significant exposure bias in UMMs during long-context, multi-turn generation, with quality declining across dialogue turns.
- Test-time scaling strategies like Chain-of-Thought improve generation accuracy and mitigate exposure bias.
- Mainstream open-source and closed-source UMMs still have substantial room for improvement in multi-turn interactions.
Significance
IMUG-Bench fills the gap in existing benchmarks for evaluating multi-turn image-text dialogues, advancing UMMs' development in real-world applications. It provides directions for enhancing future models' robustness and multi-turn interaction capabilities.
Technical Contribution
Introduces a new multi-turn interaction evaluation framework, reveals exposure bias in UMMs' generation tasks, and validates the effectiveness of various test-time scaling strategies.
Novelty
IMUG-Bench is the first to systematically evaluate UMMs' understanding and generation capabilities in multi-turn image-text dialogues, offering more realistic interaction scenarios.
Limitations
- UMMs exhibit significant exposure bias in generation tasks, with quality declining across dialogue turns.
- Current models still have substantial room for improvement in long-context generation tasks.
Future Work
Future research can explore more test-time scaling strategies to further enhance UMMs' multi-turn interaction capabilities and generation quality.
AI Executive Summary
In recent years, multimodal models have made significant progress in understanding and generation tasks, yet existing benchmarks fail to effectively evaluate dynamic interaction capabilities in multi-turn image-text dialogues. IMUG-Bench evaluates UMMs' understanding and generation capabilities through multi-turn interactions, covering Static Spatial, Temporal Causal, and Hybrid tasks. Experiments reveal significant exposure bias in UMMs during long-context, multi-turn generation, with quality declining across dialogue turns. Test-time scaling strategies like Chain-of-Thought improve generation accuracy and mitigate exposure bias. IMUG-Bench fills the gap in existing benchmarks for evaluating multi-turn image-text dialogues, advancing UMMs' development in real-world applications. Future research can explore more test-time scaling strategies to further enhance UMMs' multi-turn interaction capabilities and generation quality.
Deep Analysis
Background
Multimodal learning models have significantly improved in understanding and generation tasks, particularly with the shift from large language models to multimodal large language models. However, existing generative models and understanding models have developed independently, making unified integration challenging.
Core Problem
Existing benchmarks fail to effectively evaluate dynamic interaction capabilities in multi-turn image-text dialogues, often limited to single-turn or static settings, overlooking exposure bias in multi-turn interactions.
Innovation
IMUG-Bench evaluates UMMs' understanding and generation capabilities through multi-turn interactions, covering Static Spatial, Temporal Causal, and Hybrid tasks. Samples are generated using LLM and VLM template filling, with two-person verification ensuring quality.
Methodology
- �� Design question templates covering Static Spatial, Temporal Causal, and Hybrid tasks.
- �� Use LLM and VLM to fill templates and generate samples.
- �� Two-person verification ensures sample quality.
- �� Employ various test-time scaling strategies to improve generation accuracy.
Experiments
Experiments systematically evaluate mainstream open-source and closed-source UMMs, revealing exposure bias in long-context, multi-turn generation tasks. Test-time scaling strategies like Chain-of-Thought improve generation accuracy.
Results
Experiments reveal significant exposure bias in UMMs during long-context, multi-turn generation, with quality declining across dialogue turns. Test-time scaling strategies like Chain-of-Thought improve generation accuracy and mitigate exposure bias.
Applications
IMUG-Bench can be used to evaluate UMMs' understanding and generation capabilities in multi-turn image-text dialogues, advancing their development in real-world applications.
Limitations & Outlook
Current models still have substantial room for improvement in long-context generation tasks, with significant exposure bias affecting generation quality.
Plain Language Accessible to non-experts
Imagine you're in a multimodal dialogue, like chatting with a friend. You can communicate through text and images, and your friend can understand what you say and generate images based on your requests. IMUG-Bench acts like a test to see if your friend can maintain high-quality communication and image generation across multiple dialogue turns.
ELI14 Explained like you're 14
Imagine you're chatting with a friend who can not only understand what you say but also draw pictures based on your requests. IMUG-Bench is like a test to see if your friend can keep up high-quality communication and image generation across multiple dialogue turns. It's like a super chat bot that can help you draw what you want in complex conversations.
Glossary
UMM (Unified Multimodal Model)
A framework supporting both understanding and generation, integrating multiple modalities for interaction.
Used to evaluate understanding and generation capabilities in multi-turn image-text dialogues.
Exposure Bias
A problem where generation quality declines across dialogue turns.
Significantly impacts generation quality in multi-turn tasks.
Chain-of-Thought
A test-time scaling strategy improving generation accuracy.
Used to mitigate exposure bias in multi-turn generation tasks.
VLM (Vision-Language Model)
A model combining visual and language information for task processing.
Used to fill question templates in IMUG-Bench.
LLM (Large Language Model)
A large-scale model processing language information.
Used to fill question templates in IMUG-Bench.
Open Questions Unanswered questions from this research
- 1 How to further mitigate exposure bias in multi-turn generation tasks?
- 2 How can current models improve in long-context generation tasks?
Applications
Immediate Applications
Multi-turn Dialogue Evaluation
Used to evaluate UMMs' understanding and generation capabilities in multi-turn image-text dialogues.
Long-term Vision
Multimodal Interaction Development
Advances UMMs' development in real-world applications, enhancing multi-turn interaction capabilities.
Abstract
In recent years, unified multimodal models (UMMs) have emerged to support both understanding and generation within a single framework. Mastering dynamic, multi-turn interleaved image-text dialogues is a crucial task for UMMs in real-world applications. However, existing benchmarks fail to evaluate this important task, as they are often limited to single-turn or static settings, and typically overlook exposure bias in multi-turn interactions. To bridge this gap, we propose IMUG-Bench, a comprehensive benchmark for multi-turn interleaved image-text dialogue of UMMs that jointly evaluates their understanding and generation capabilities. Our IMUG-Bench comprises three classes: Static Spatial, Temporal Causal, and Hybrid, covering 3,113 samples and 12,034 interaction turns. It also includes dynamic understanding questions, thereby supporting evaluation that better reflects real-world multi-turn interaction scenarios. Large-scale experiments on IMUG-Bench systematically evaluate mainstream open-source and closed-source UMMs, revealing their capability boundaries and failure modes, and uncovering pronounced exposure bias on the generation side in multi-turn interactions. We further explore several test-time scaling strategies, including Chain-of-Thought, Self-Verification, and Best-of-N Sampling, which effectively improve generation accuracy and mitigate exposure bias in generation tasks. These findings provide insights into enhancing the robustness and multi-turn interaction capability of future UMMs.