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README.md
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### Key Features
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* π **Most trustworthy LLaVA 1.5**: By learning from open-source AI feedback, specifically, the feedback from LLaVA-NeXT-34B, RLAIF-V-7B achieves the best trustworthiness improvement on LLaVA-v1.5 compared to other hallucination reduction methods.
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* πͺ **Maintaining Well Performance on General Abilities**: On benchmarks evaluating general capabilities (e.g.
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<p align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6566e0c493e30c8a60048eb3/
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</p>
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### Examples
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}
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@article{yu2024rlaifv,
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title={RLAIF-V:
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author={Yu
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journal={arXiv preprint arXiv:2405.17220},
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year={2024},
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}
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### Key Features
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* π **Most trustworthy LLaVA 1.5**: By learning from open-source AI feedback, specifically, the feedback from LLaVA-NeXT-34B, RLAIF-V-7B achieves the best trustworthiness improvement on LLaVA-v1.5 compared to other hallucination reduction methods.
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* πͺ **Maintaining Well Performance on General Abilities**: On benchmarks evaluating general capabilities (e.g. MMStar), RLAIF-V-7B also exhibits good performance.
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* π **Inference-time Scaling by Self-guidance**: Using RLAIF-V 7B as a reward model can further improve model performance on multiple benchmarks with best-of-N selection.
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<p align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6566e0c493e30c8a60048eb3/dhsi5_okbtlBp2pfYOkFK.png" alt="fig1" width="90%"/>
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</p>
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### Examples
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}
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@article{yu2024rlaifv,
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title={RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness},
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author={Tianyu Yu and Haoye Zhang and Qiming Li and Qixin Xu and Yuan Yao and Da Chen and Xiaoman Lu and Ganqu Cui and Yunkai Dang and Taiwen He and Xiaocheng Feng and Jun Song and Bo Zheng and Zhiyuan Liu and Tat-Seng Chua and Maosong Sun},
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journal={arXiv preprint arXiv:2405.17220},
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year={2024},
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}
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