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@@ -37,15 +37,15 @@ demonstrate their fine-tuning potential in various downstream tasks.
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  - SightationVQA
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  - SightationReasoning
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- <img src="https://cdn-uploads.huggingface.co/production/uploads/67a86f66c6f66e2fa5888b41/cNshK4QAdiNMqk7x6J6j7.png" width="70%" height="70%" title="visual_abstract" alt="visual_abstract"></img>
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  The key benefit of utilizing sighted user feedback lies in their assessments that are based on solid visual
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  grounding. The compiled assessments prove an effective training substance for steering VLMs towards more
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  accessible descriptions.
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- <img src="https://cdn-uploads.huggingface.co/production/uploads/67a86f66c6f66e2fa5888b41/8oYvtq7dtv_Ck-U6OlcAE.png" width="50%" height="50%" title="dimensions_assignment" alt="dimensions_assignment"></img>
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  The description qualities assessed by their respective evaluator groups.
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  ## Results
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- <img src="https://cdn-uploads.huggingface.co/production/uploads/67a86f66c6f66e2fa5888b41/094e9Hw7lauvT1tshg1Wj.png" width="60%" height="60%" title="spider_chart" alt="spider_chart"></img>
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  Tuning VLMs on Sightation enhanced various qualities of the diagram descriptions, evaluated by BLV educators, and shown here as normalized ratings averaged in each aspect.
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  The capability of the dataset is most strongly pronounced with Qwen2-VL-2B model, shown above.
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  - SightationVQA
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  - SightationReasoning
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+ <img src="https://cdn-uploads.huggingface.co/production/uploads/67a86f66c6f66e2fa5888b41/cNshK4QAdiNMqk7x6J6j7.png" width="80%" height="80%" title="visual_abstract" alt="visual_abstract"></img>
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  The key benefit of utilizing sighted user feedback lies in their assessments that are based on solid visual
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  grounding. The compiled assessments prove an effective training substance for steering VLMs towards more
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  accessible descriptions.
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+ <img src="https://cdn-uploads.huggingface.co/production/uploads/67a86f66c6f66e2fa5888b41/8oYvtq7dtv_Ck-U6OlcAE.png" width="70%" height="70%" title="dimensions_assignment" alt="dimensions_assignment"></img>
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  The description qualities assessed by their respective evaluator groups.
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  ## Results
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+ <img src="https://cdn-uploads.huggingface.co/production/uploads/67a86f66c6f66e2fa5888b41/094e9Hw7lauvT1tshg1Wj.png" width="80%" height="80%" title="spider_chart" alt="spider_chart"></img>
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  Tuning VLMs on Sightation enhanced various qualities of the diagram descriptions, evaluated by BLV educators, and shown here as normalized ratings averaged in each aspect.
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  The capability of the dataset is most strongly pronounced with Qwen2-VL-2B model, shown above.
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