  Interleaved text-and-image generation has been an intriguing research
direction, where the models are required to generate both images and text
pieces in an arbitrary order. Despite the emerging advancements in interleaved
generation, the progress in its evaluation still significantly lags behind.
Existing evaluation benchmarks do not support arbitrarily interleaved images
and text for both inputs and outputs, and they only cover a limited number of
domains and use cases. Also, current works predominantly use similarity-based
metrics which fall short in assessing the quality in open-ended scenarios. To
this end, we introduce InterleavedBench, the first benchmark carefully curated
for the evaluation of interleaved text-and-image generation. InterleavedBench
features a rich array of tasks to cover diverse real-world use cases. In
addition, we present InterleavedEval, a strong reference-free metric powered by
GPT-4o to deliver accurate and explainable evaluation. We carefully define five
essential evaluation aspects for InterleavedEval, including text quality,
perceptual quality, image coherence, text-image coherence, and helpfulness, to
ensure a comprehensive and fine-grained assessment. Through extensive
experiments and rigorous human evaluation, we show that our benchmark and
metric can effectively evaluate the existing models with a strong correlation
with human judgments surpassing previous reference-based metrics. We also
provide substantial findings and insights to foster future research in
interleaved generation and its evaluation.
