Duplicate from mistralai/MM-MT-Bench

Co-authored-by: Soham Ghosh <sohamghosh121@users.noreply.huggingface.co>

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README.md

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+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: conversation
+    dtype: string
+  - name: category
+    dtype: string
+  splits:
+  - name: eval
+    num_bytes: 36977825.0
+    num_examples: 92
+  download_size: 36774506
+  dataset_size: 36977825.0
+configs:
+- config_name: default
+  data_files:
+  - split: eval
+    path: data/eval-*
+---
+
+# MM-MT-Bench
+
+MM-MT-Bench is a multi-turn LLM-as-a-judge evaluation benchmark similar to the text MT-Bench for testing multimodal instruction-tuned models. While existing benchmarks like MMMU, MathVista, ChartQA and so on are focused on closed-ended questions with short responses, they do not evaluate model's ability to follow user instructions in multi-turn dialogues and answer open-ended questions in a zero-shot manner. MM MT-Bench is designed to overcome this limitation. The [mistral-evals](https://github.com/mistralai/mistral-evals) repository provides an example script on how to evaluate a model on this benchmark.
+
+[Paper link](https://arxiv.org/abs/2410.07073)

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