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<!DOCTYPE html>
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content="RLHF-V">
<meta name="keywords" content="RLHF-V, open-source, vision-language, MLLM">
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<title>RLHF-V</title>
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<h1 class="title is-1 publication-title">RLHF-V</h1>
<h2 class="title is-3 publication-title">Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback</h2>
<div class="is-size-5">
<span class="author-block">
<a href="https://github.com/yiranyyu" style="color:#008AD7;font-weight:normal;">Tianyu Yu<sup>1</sup>
</a>,
</span>
<span class="author-block">
<a href="https://yaoyuanthu.github.io/" style="color:#008AD7;font-weight:normal;">Yuan Yao<sup>2*</sup></a>,
</span>
<span class="author-block">
Haoye Zhang<sup>1</sup>,
</span>
<span class="author-block">
Taiwen He<sup>1</sup>,
</span>
<span class="author-block">
Yifeng Han<sup>1</sup>,
</span>
<span class="author-block">
<a href="https://cgq15.github.io" style="color:#008AD7;font-weight:normal;">Ganqu Cui<sup>1</sup></a>,
</span>
<span class="author-block">
<a href="https://jameshujy.github.io/" style="color:#008AD7;font-weight:normal;">Jinyi Hu<sup>1</sup></a>,
</span>
<br>
<span class="author-block">
<a href="http://nlp.csai.tsinghua.edu.cn/~lzy/" style="color:#008AD7;font-weight:normal;">Zhiyuan Liu<sup>1*</sup></a>,
</span>
<span class="author-block">
<a href="https://ke.sigs.tsinghua.edu.cn/main.psp" style="color:#008AD7;font-weight:normal;">Hai-Tao Zheng<sup>1*</sup></a>,
</span>
<span class="author-block">
<a href="https://www.cs.tsinghua.edu.cn/csen/info/1180/4033.htm" style="color:#008AD7;font-weight:normal;">Maosong Sun<sup>1</sup></a>
</span>
<span class="author-block">
<a href="https://www.chuatatseng.com/" style="color:#008AD7;font-weight:normal;">Tat-Seng Chua<sup>2</sup></a>
</span>
</div>
<br>
<div class="is-size-5 publication-authors">
<span class="author-block"><b style="color:#F2A900; font-weight:normal">▶ </b>1. Tsinghua University </span>
<br>
<span class="author-block"><b style="color:#00A4EF; font-weight:normal">▶ </b>2. National University of Singapore </span>
<!-- <span class="author-block"><b style="color:#00A4EF; font-weight:normal">▶ </b>Microsoft Research, Redmond; </span> -->
<!-- <span class="author-block"><b style="color:#008AD7; font-weight:normal">▶ </b>Microsoft Cloud & AI </span> -->
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>*</sup>Correspondence</span>
<!-- <span class="author-block"><sup>☨</sup>Equal Advisory Contribution, </span> -->
<!-- <span class="author-block"><sup>⚑</sup>Project Lead </span> -->
</div>
<br>
<!-- <div class="is-size-5 publication-authors">
<span class="author-block"><b style="color:#e08ba0; font-weight:normal"> <b>In CVPR2023</b> </b></span>
</div> -->
<div class="column has-text-centered">
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<span class="link-block">
<a href="https://arxiv.org/abs/2312.00849" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>Paper</span>
</a>
</span>
<span class="link-block">
<a href="https://github.com/RLHF-V/RLHF-V" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<!-- <span class="link-block">-->
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<span>Demo</span>
</a>
</span>
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class="external-link button is-normal is-rounded is-dark">
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<i class="fa fa-database"></i>
</span>
<span>Dataset</span>
</a>
</span>
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class="external-link button is-normal is-rounded is-dark">
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<i class="fa fa-laugh"></i>
</span>
<span>Model</span>
</a>
</span>
</div>
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</div>
</div>
</div>
</div>
</section>
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<p>
Thanks for your interest in our work. Currently, the number of users has exceeded our expectations. We provide <strong><font color="#008AD7">alternative demo links</font></strong> here:
<a href="https://b2517615b965687635.gradio.live" target="_blank">Demo1</a>
<a href="https://c8de8ff74b6a6c6a9b.gradio.live" target="_blank">Demo2</a>
<a href="https://90bc0bac96e6457e8f.gradio.live" target="_blank">Demo3</a>
<a href="https://cd772059965a71f9e6.gradio.live" target="_blank">Demo4</a>
<a href="https://48da7e23bcadec7551.gradio.live" target="_blank">Demo5</a>
<a href="https://687d119023cd37e5fb.gradio.live" target="_blank">Demo6</a>
<a href="https://0810e8582bcad31944.gradio.live" target="_blank">Demo7</a>
<a href="https://31c7cdb7e3594e851e.gradio.live" target="_blank">Demo8</a>
<strong><font>News</font></strong>: We now provide a pretrained MiniGPT-4 aligned with <strong><font color="#008AD7">Vicuna-7B</font></strong>! The demo GPU memory consumption now can be <strong><font color="#008AD7">as low as 12GB</font></strong>.
<br>
</p>
</div>
</div>
</div>
</section>
-->
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<div class="container">
<!-- Abstract. -->
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<br>
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified" style="font-size: large;">
<p>
Existing Multimodal Large Language Models prevalently suffer from serious <b>hallucination</b> problems, generating text that is not factually grounded in associated images. Our <b>RLHF-V framework</b> enhances MLLM trustworthiness via behavior alignment from fine-grained correctional human feedback.
<ul>
<li>
<b style="font-family:Arial, Helvetica, sans-serif">Fine-Grained and Diverse Human Preference Data</b>: <span style="font-size: 95%;">We collect 1.4K fine-grained human feedback consisting of 3.7k pieces of segment-level corrections, covering hallucination types including objects (41.2%), positions (20.3%), numbers (16.5%), attributes (10%), actions (5.3%), and others (6.8%).</span>
</li>
<li>
<b style="font-family:Arial, Helvetica, sans-serif">High Data Efficiency and Scalability</b>: <span style="font-size: 95%;">With just 1.4K annotated data, we achieve a 34.8% reduction in model hallucinations. Moreover, the decrease in hallucinations becomes more significant as more data used.</span>
</li>
<li>
<b style="font-family:Arial, Helvetica, sans-serif">Enhanced Performance and Computational Efficiency</b>: <span style="font-size: 95%;">Our fine-grained correctional human feedback data can better credit the desired behavior, allowing efficient training in 1 hour on 8 A100 GPUs to achieve promising results.</span>
</li>
<li>
<b style="font-family:Arial, Helvetica, sans-serif">Outstanding Trustworthiness without Compromising Helpfulness</b>: <span style="font-size: 95%;">Our model surpasses existing open-source MLLMs in reducing hallucination rates, mitigates hallucination from over-generalization, and maintains informativeness. Surprisingly, RLHF-V is even more resistant to the over-generalization problem compared with <a href="https://openai.com/research/gpt-4v-system-card">GPT-4V</a>.</span>
</li>
</ul>
<!-- <br>
The
</b> -->
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
<!-- <img id="model" width="100%" src="images/rlhf-v-main_exp.jpg"> -->
<!-- <h3 class="subtitle has-text-centered">
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<br>
<!-- Paper video. -->
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<div class="publication-video">
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<!--/ Demo. -->
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<img id="teaser" width="90%" src="images/demo6_AdobeExpress.gif">
</div>
<div class="columns is-centered">
<h1>
<p style="font-family:Times New Roman"><b>X-GPT: Connecting generalist X-Decoder with GPT-3</b>
</h1>
</div>
</div>
<br>
<div class="column is-full-width">
<div class="columns is-centered">
<img id="teaser" width="90%" src="images/inpaint.gif">
</div>
<div class="columns is-centered">
<h1>
<p style="font-family:Times New Roman"><b>Instruct-X-Decoder: Object-centric instructional image editing</b>
</h1>
</div>
</div> -->
<section class="section">
<div class="container is-max-desktop">
<!--/ Paper video. -->
<br>
<br>
<!-- Paper Model. -->
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths">
<h2 class="title is-3">Method</h2>
<div class="content has-text-justified">
<p>
<b>The proposed RLHF-V framework</b>:
</p>
<p>
We collect <a href="https://huggingface.co/datasets/HaoyeZhang/RLHF-V-Hall_v0/tree/main">1.4k fine-grained dense feedback data</a> by asking human annotators to correct the hallucinated segments in model responses.
The training takes only 1 hour with 8 A100 GPUs to get <a href="https://huggingface.co/openbmb/RLHF-V_v0">RLHF-V-13B</a> which is initialized from our <a href="https://huggingface.co/Yirany/RLHF-V_v0_SFT/tree/main">RLHF-V_SFT-13B</a>.
</p>
<ul>
<!-- <li>It has two types of queries (latent queries and text queries) and outputs (semantic outputs and pixel-level outputs).</li>
<li>It uses a single text encoder for all text corpus, ranging from class concepts, referring phrases to image captions.</li>
<li>It decouples image and text encoder to accomadate cross-image tasks (e.g., image-text retrieval) and within-image tasks (e.g., segmentation and captioning).</li> -->
</ul>
</div>
<img id="model" width="100%" src="images/rlhf-v_framework.jpg", alt="Illustration of the RLHF-V frmework">
<!-- <h3 class="subtitle has-text-centered">
<p style="font-family:Times New Roman"></p>
</h3> -->
<br>
<br>
</div>
</div>
<br>
<br>
<!--/ Paper video. -->
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths">
<h2 class="title is-3">Highlights</h2>
<br>
<div class="content has-text-justified">
<p>
<b>Low hallucination rate while being informative</b>:
</p>
<ul>
<!-- <li>It has two types of queries (latent queries and text queries) and outputs (semantic outputs and pixel-level outputs).</li>
<li>It uses a single text encoder for all text corpus, ranging from class concepts, referring phrases to image captions.</li>
<li>It decouples image and text encoder to accomadate cross-image tasks (e.g., image-text retrieval) and within-image tasks (e.g., segmentation and captioning).</li> -->
</ul>
</div>
<img id="model" width="80%" src="images/rlhf-v-main_exp.jpg" alt="Main experimental results of RLHF-V">
<!-- <h3 class="subtitle has-text-centered">
<p style="font-family:Times New Roman"></p>
</h3> -->
<br>
<br>
<div class="content has-text-justified">
<p>
<b>Data-efficient and showing good scaling results</b>:
</p>
</div>
<img id="model" width="40%" src="images/data_scaling.png">
<br>
<br>
<div class="content has-text-justified">
<p>
<b>More resistant to over-generalization</b>:
</p>
</div>
<img id="model" width="80%" src="images/over-generalization.jpg">
<br>
<br>
</div>
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<div class="columns is-centered has-text-centered">
<h2 class="title is-3">BibTeX</h2>
</div>
<pre><code>
@article{2023rlhf-v,
author = {Tianyu Yu and Yuan Yao and Haoye Zhang and Taiwen He and Yifeng Han and Ganqu Cui and Jinyi Hu and Zhiyuan Liu and Hai-Tao Zheng and Maosong Sun and Tat-Seng Chua},
title = {RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback},
journal = {arxiv},
year = {2023},
}
</code></pre>
</div>
<br>
</section>
<section class="section">
<!-- Results. -->
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths">
<h2 class="title is-3">Examples</h2>
</div>
</div>
<!--/ Results. -->
<div class="container is-max-desktop">
<!-- <section class="section"> -->
<!-- <div id="main">
<div align="center" ><div align="center" ><img src="demos/case1.png" width="75%" alt=""></div></div>
<div align="center" ><div align="center" ><img src="demos/case2.png" width="75%" alt=""></div></div>
<div align="center" ><div align="center" ><img src="demos/case3-1.png" width="75%" alt=""></div></div>
<div align="center" ><div align="center" ><img src="demos/case3-2.png" width="75%" alt=""></div></div>
<div align="center" ><div align="center" ><img src="demos/case4.png" width="75%" alt=""></div></div>
<div align="center" ><div align="center" ><img src="demos/case5.png" width="75%" alt=""></div></div>
<!-- <div class="box"><div class="pic"><img src="demos/p7.png" alt=""></div></div>
<div class="box"><div class="pic"><img src="demos/p8.png" alt=""></div></div>
<div class="box"><div class="pic"><img src="demos/p9.png" alt=""></div></div>
</div> -->
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths">
<div class="content has-text-justified">
<ul>
<li><b>Short-form QA</b>: RLHF-V can give a more trustworthy answer in short-form QA.</li>
</ul>
</div>
<img src="demos/case1.png" width="90%" alt="">
<div class="content has-text-justified">
<ul>
<li><b>Long-form QA</b>: RLHF-V can generate informative image description with less hallucinations.</li>
</ul>
</div>
<img src="demos/case2.png" width="90%" alt="">
<div class="content has-text-justified">
<ul>
<li><b>Long-form QA</b>: RLHF-V can provide detailed reasoning with less hallucinations.</li>
</ul>
</div>
<img src="demos/case3-1.png" width="90%" alt="">
<img src="demos/case3-2.png" width="90%" alt="">
<div class="content has-text-justified">
<ul>
<li><b>Long-form QA</b>: RLHF-V is more resistant to over-generalization.</li>
</ul>
</div>
<img src="demos/case4.png" width="90%" alt="">
<div class="content has-text-justified">
<ul>
<li><b>Long-form QA</b>: RLHF-V is more resistant to over-generalization.</li>
</ul>
</div>
<img src="demos/case5.png" width="90%" alt="">
</div>
<!-- <div class="box"><div class="pic"><img src="demos/p7.png" alt=""></div></div>
<div class="box"><div class="pic"><img src="demos/p8.png" alt=""></div></div>
<div class="box"><div class="pic"><img src="demos/p9.png" alt=""></div></div> -->
</div>
</div>
</section>
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<h2 class="title is-3">Acknowledgement</h2>
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href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
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