A High-Quality Dataset and Reliable Evaluation for Interleaved
  Image-Text Generation
Recent advancements in Large Multimodal Models (LMMs) have significantly
improved multimodal understanding and generation. However, these models still
struggle to generate tightly interleaved image-text outputs, primarily due to
the limited scale, quality and instructional richness of current training
datasets. To address this, we introduce InterSyn, a large-scale multimodal
dataset constructed using our Self-Evaluation with Iterative Refinement (SEIR)
method. InterSyn features multi-turn, instruction-driven dialogues with tightly
interleaved imagetext responses, providing rich object diversity and rigorous
automated quality refinement, making it well-suited for training
next-generation instruction-following LMMs. Furthermore, to address the lack of
reliable evaluation tools capable of assessing interleaved multimodal outputs,
we introduce SynJudge, an automatic evaluation model designed to quantitatively
assess multimodal outputs along four dimensions: text content, image content,
image quality, and image-text synergy.
  Experimental studies show that the SEIR method leads to substantially higher
dataset quality compared to an otherwise identical process without refinement.
  Moreover, LMMs trained on InterSyn achieve uniform performance gains across
all evaluation metrics, confirming InterSyn's utility for advancing multimodal
systems.
684fa6d060b4a34dbe007aa7
Yukang Feng
admin_assigned
2025-06-16T07:48:14.381Z
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fengyukang
user
finyorko
684fa6d060b4a34dbe007aa8
Jianwen Sun
684fa6d060b4a34dbe007aa9
Chuanhao Li
claimed_verified
2025-06-23T08:30:42.314Z
674ea59a8f2e7614a6c72f26
/avatars/86fd4c6d7d33435de49b35659bf65265.svg
Chuanhao
user
ChuanhaoLi
684fa6d060b4a34dbe007aaa
Zizhen Li
684fa6d060b4a34dbe007aab
Jiaxin Ai
684fa6d060b4a34dbe007aac
Fanrui Zhang
admin_assigned
2025-06-16T07:49:04.386Z
665305eff0c8c891cae7fe01
/avatars/1f372e3bc6a4eb19ef702ec96a391c96.svg
Fanrui Zhang
user
fanrui00
684fa6d060b4a34dbe007aad
Yifan Chang
684fa6d060b4a34dbe007aae
Sizhuo Zhou
684fa6d060b4a34dbe007aaf
Shenglin Zhang
admin_assigned
2025-06-16T07:49:28.741Z
6674d02914e2aebef893779e
/avatars/acdbe3820462b87126c8f1e14f0d1a60.svg
ZhangShenglin
user
ZhangShenglin
684fa6d060b4a34dbe007ab0
Yu Dai
684fa6d060b4a34dbe007ab1
Kaipeng Zhang
admin_assigned
2025-06-16T07:49:35.126Z
63527f4e7d071f23d085ad45
/avatars/99a51adef5673b3ac1a8c02eb47759c4.svg
KAIPENG ZHANG
user
kpzhang
A High-Quality Dataset and Reliable Evaluation for Interleaved
  Image-Text Generation
Recent advancements in Large Multimodal Models (LMMs) have significantly
improved multimodal understanding and generation. However, these models still
struggle to generate tightly interleaved image-text outputs, primarily due to
the limited scale, quality and instructional richness of current training
datasets. To address this, we introduce InterSyn, a large-scale multimodal
dataset constructed using our Self-Evaluation with Iterative Refinement (SEIR)
method. InterSyn features multi-turn, instruction-driven dialogues with tightly
interleaved imagetext responses, providing rich object diversity and rigorous
automated quality refinement, making it well-suited for training
next-generation instruction-following LMMs. Furthermore, to address the lack of
reliable evaluation tools capable of assessing interleaved multimodal outputs,
we introduce SynJudge, an automatic evaluation model designed to quantitatively
assess multimodal outputs along four dimensions: text content, image content,
image quality, and image-text synergy.
  Experimental studies show that the SEIR method leads to substantially higher
dataset quality compared to an otherwise identical process without refinement.
  Moreover, LMMs trained on InterSyn achieve uniform performance gains across
all evaluation metrics, confirming InterSyn's utility for advancing multimodal
systems.
InterSyn, a large-scale dataset with tightly interleaved image-text outputs and automated quality refinement, improves multimodal understanding and generation through the SEIR method and SynJudge, an automatic evaluation tool.
Large Multimodal Models (LMMs) multimodal understanding multimodal generation Self-Evaluation with Iterative Refinement (SEIR) InterSyn image-text outputs SynJudge text content image content image quality image-text synergy