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EVOKE

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kailinjiang commited on Jun 3

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 To evaluate evolving knowledge injection in LMMs, we propose a pipeline to automatically collect evolving knowledge, constructing the <u><b>EVO</b></u>lving <u><b>K</b></u>nowledg<u><b>E</b></u> <b>(EVOKE)</b> benchmark. The <b>EVOKE</b> benchmark comprises <strong><span style="color:brown">9,422</span></strong> knowledge-image pairs for LMM knowledge injection, spanning <strong><span style="color:brown">159</span></strong> fine-grained types (<strong><span style="color:brown">29</span></strong> New types and <strong><span style="color:brown">130</span></strong> Entity types), highlighting its diversity.
 </p>
-<!-- This dataset is used for our work: [MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge](https://hf.co/papers/2502.19870), and our code has been released on [GitHub here](https://github.com/MMKE-Bench-ICLR/MMKE-Bench). -->
 You can download **EVOKE** 🤗. And the expected structure of files is:

 To evaluate evolving knowledge injection in LMMs, we propose a pipeline to automatically collect evolving knowledge, constructing the <u><b>EVO</b></u>lving <u><b>K</b></u>nowledg<u><b>E</b></u> <b>(EVOKE)</b> benchmark. The <b>EVOKE</b> benchmark comprises <strong><span style="color:brown">9,422</span></strong> knowledge-image pairs for LMM knowledge injection, spanning <strong><span style="color:brown">159</span></strong> fine-grained types (<strong><span style="color:brown">29</span></strong> New types and <strong><span style="color:brown">130</span></strong> Entity types), highlighting its diversity.
 </p>
+https://arxiv.org/abs/2505.24449
 You can download **EVOKE** 🤗. And the expected structure of files is: