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<!DOCTYPE html>
<!-- saved from url=(0027)https://llava-vl.github.io/ -->
<html class="fontawesome-i2svg-active fontawesome-i2svg-complete" lang="en-US"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta name="description" content="Vision-Language Feedback">
<meta name="keywords" content="multimodal chatbot">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Silkie</title>
<style 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.fa-rotate-90{-webkit-filter:none;filter:none}.fa-stack{display:inline-block;height:2em;position:relative;width:2.5em}.fa-stack-1x,.fa-stack-2x{bottom:0;left:0;margin:auto;position:absolute;right:0;top:0}.svg-inline--fa.fa-stack-1x{height:1em;width:1.25em}.svg-inline--fa.fa-stack-2x{height:2em;width:2.5em}.fa-inverse{color:#fff}.sr-only{border:0;clip:rect(0,0,0,0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px}.sr-only-focusable:active,.sr-only-focusable:focus{clip:auto;height:auto;margin:0;overflow:visible;position:static;width:auto}.svg-inline--fa .fa-primary{fill:var(--fa-primary-color,currentColor);opacity:1;opacity:var(--fa-primary-opacity,1)}.svg-inline--fa .fa-secondary{fill:var(--fa-secondary-color,currentColor);opacity:.4;opacity:var(--fa-secondary-opacity,.4)}.svg-inline--fa.fa-swap-opacity .fa-primary{opacity:.4;opacity:var(--fa-secondary-opacity,.4)}.svg-inline--fa.fa-swap-opacity .fa-secondary{opacity:1;opacity:var(--fa-primary-opacity,1)}.svg-inline--fa mask .fa-primary,.svg-inline--fa mask .fa-secondary{fill:#000}.fad.fa-inverse{color:#fff}</style><link rel="stylesheet" href="./LLaVA_files/css">
<link rel="stylesheet" href="./LLaVA_files/bulma.min.css">
<link rel="stylesheet" href="./LLaVA_files/bootstrap.min.css">
<link rel="stylesheet" href="./LLaVA_files/academicons.min.css">
<link rel="stylesheet" href="./LLaVA_files/all.min.css">
<link rel="stylesheet" href="./LLaVA_files/index.css">
<link rel="icon" href="./LLaVA_files/silkie.png">
<link href="./LLaVA_files/icon" rel="stylesheet">
<script src="./LLaVA_files/jquery.min.js"></script>
<script defer="" src="./LLaVA_files/all.min.js"></script>
<script type="module" src="./LLaVA_files/gradio.js"></script>
<style>
.expandable-card .card-text-container {
max-height: 200px;
overflow-y: hidden;
position: relative;
}
.expandable-card.expanded .card-text-container {
max-height: none;
}
.expand-btn {
position: relative;
display: none;
background-color: rgba(255, 255, 255, 0.8);
/* margin-top: -20px; */
/* justify-content: center; */
color: #510c75;
border-color: transparent;
}
.expand-btn:hover {
background-color: rgba(200, 200, 200, 0.8);
text-decoration: none;
border-color: transparent;
color: #510c75;
}
.expand-btn:focus {
outline: none;
text-decoration: none;
}
.expandable-card:not(.expanded) .card-text-container:after {
content: "";
position: absolute;
bottom: 0;
left: 0;
width: 100%;
height: 90px;
background: linear-gradient(rgba(255, 255, 255, 0.2), rgba(255, 255, 255, 1));
}
.expandable-card:not(.expanded) .expand-btn {
margin-top: -40px;
}
.card-body {
padding-bottom: 5px;
}
.vertical-flex-layout {
justify-content: center;
align-items: center;
height: 100%;
display: flex;
flex-direction: column;
gap: 5px;
}
.figure-img {
max-width: 100%;
height: auto;
}
.adjustable-font-size {
font-size: calc(0.5rem + 2vw);
}
.chat-history {
flex-grow: 1;
overflow-y: auto;
/* overflow-x: hidden; */
padding: 5px;
border-bottom: 1px solid #ccc;
margin-bottom: 10px;
}
#gradio pre {
background-color: transparent;
}
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<!-- <h1 class="title is-1 publication-title">VLFeedback and Silkie</h1> -->
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<img src="./LLaVA_files/silkie.png" alt="VLFeedback and Silkie" style="height: 50px; width: 50px;"> VLFeedback and Silkie
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<h1 class="title is-1 publication-title">
<span style="display: inline-block; vertical-align: middle;">VLFeedback</span>
<span style="display: inline-block; vertical-align: middle; margin-top: -15px; margin-left: -7px;">
<img src="./LLaVA_files/silkie.png" alt="VLFeedback and Silkie" style="height: 50px; width: 50px;">
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</h1>
<h3 class="title is-3 publication-title">A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment</h3>
<div class="is-size-5">
<span class="author-block">
<a href="https://lilei-nlp.github.io" style="color:#008AD7;font-weight:normal;">Lei Li<sup>*</sup></a>,
</span>
<span class="author-block">
<a href="https://zhxie.site/" style="color:#008AD7;font-weight:normal;">Zhihui Xie<sup>*</sup></a>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=BizedOAAAAAJ" style="color:#008AD7;font-weight:normal;">Mukai Li</a>,
</span> <br>
<span class="author-block">
<a href="https://github.com/Shunian-Chen" style="color:#F2A900;font-weight:normal;">Shunian Chen</a>,
</span>
<span class="author-block">
<a href="https://scholar.google.com.tw/citations?user=K0uQ3ygAAAAJ" style="color:#94070A;font-weight:normal;">Peiyi Wang</a>,
</span>
<span class="author-block">
<a href="https://chenllliang.github.io/about/" style="color:#94070A;font-weight:normal;">Liang Chen</a>,
</span> <br>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=SAeMbW4AAAAJ&hl=en" style="color:#008AD7;font-weight:normal;">Yazheng Yang</a>,
</span>
<span class="author-block">
<a href="https://wabyking.github.io/old.html" style="color:#F2A900;font-weight:normal;">Benyou Wang</a>,
</span>
<span class="author-block">
<a href="https://ikekonglp.github.io/" style="color:#008AD7;font-weight:normal;">Lingpeng Kong</a>,
</span>
<span class="author-block">
<a href="https://leuchine.github.io/" style="color:#008AD7;font-weight:normal;">Qi Liu</a>
</span>
</span>
</div>
<br>
<div class="is-size-5 publication-authors">
<span class="author-block"><b style="color:#008AD7; font-weight:normal">▶ </b>The University of Hong Kong</span>
<br>
<span class="author-block"> <b style="color:#F2A900; font-weight:normal"> ▶ </b> The Chinese University of Hong Kong, Shenzhen </span> <br>
<span class="author-block"><b style="color:#94070A; font-weight:normal">▶ </b>Peking University</span> <br>
<span class="author-block"> <sup>*</sup>Equal Contribution</span>
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<span>arXiv</span>
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<a href="https://huggingface.co/datasets/MMInstruction/VLFeedback" target="_blank" class="external-link button is-normal is-rounded is-dark">
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<span>Dataset (VLFeedback)</span>
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<section class="hero teaser">
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<h4 class="subtitle has-text-centered" style="color:rgb(0, 4, 255)">
Accepted by EMNLP 2024 Main Conference
</h4>
<h4 class="subtitle has-text-centered">
<!-- LLaVA-RLHF represents the first open-source <strong>RLHF-trained</strong> large multimodal model for general-purpose visual and language understanding, achieving impressive visual reasoning and perception capabilities mimicking spirits of the multimodal GPT-4 and setting a new state-of-the-art accuracy on LLaVA-Bench, MMBench, and MMHal-Bench. <br> -->
<!-- We propose a new alignment algorithm called <strong>Factually Augmented RLHF (Fact-RLHF)</strong> that augments the reward model with additional factual information such as image captions and ground-truth multi-choice options, which alleviates the reward hacking phenomenon in RLHF and further improves the performance. <br> -->
<!-- LLaVA-RLHF combines a CLIP vision encoder and Vicuna, is fine-tuned with high-quality vision instruction tuning data and Fact-RLHF, and is presented to be <strong>more helpful</strong> and <strong>less hallucinated</strong> than LLaVA or other open-sourced LMMs. -->
VLFeedback is the first open-sourced <strong>GPT-4V annotated vision-language preference dataset</strong>, covering 80k instructions sampled from various sources with responses decoded from 12 large language vision models such as GPT-4V, LLaVA-series and Qwen-VL. <br>
Based on Qwen-VL-Chat, we present Silkie, by performing DPO on our VLFeedback. Compared with the original model, Silkile
<strong>achieves 6.9% and 9.5% relative improvement on the MME benchmark regarding the perception and cognition capabilities</strong>, respectively.
Besides, Silkie sets a new <strong>state-of-the-art score of 3.02 on MMHal-Bench</strong> regarding hallucination evaluation.
</h4>
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
As large vision-language models (LVLMs) evolve rapidly, the demand for high-quality and diverse data to align these models becomes increasingly crucial.
However, the creation of such data with human supervision proves costly and time-intensive.
In this paper, we investigate the efficacy of AI feedback to scale supervision for aligning LVLMs.
We introduce VLFeedback, the first large-scale vision-language feedback dataset, comprising over 82K multi-modal instructions and comprehensive rationales generated by off-the-shelf models without human annotations.
To evaluate the effectiveness of AI feedback for vision-language alignment, we train Silkie, an LVLM fine-tuned via direct preference optimization on VLFeedback.
Silkie showcases exceptional performance regarding helpfulness, visual faithfulness, and safety metrics. It outperforms its base model by 6.9% and 9.5% in perception and cognition tasks, reduces hallucination issues on MMHal-Bench, and exhibits enhanced resilience against red-teaming attacks.
Furthermore, our analysis underscores the advantage of AI feedback, particularly in fostering preference diversity to deliver more comprehensive improvements.
<p></p>
</div>
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</div>
</section>
<section class="section">
<!-- Results. -->
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<h2 class="title is-3"><img id="painting_icon" width="3%" src="./LLaVA_files/5886212.png"> Multimodal Instructions and AI Preference Data</h2>
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<!-- </div> -->
<!--/ Results. -->
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<p>
We sample multi-modal instructions from various souces, covering different capabilities of LVLMs. We further build a model pool consisting of 12 LVLMs. </a>.
<centering>
<div style="text-align: center;">
<img id="teaser" width="80%" src="./LLaVA_files/instruction_source.png">
</div>
<br>
We further use GPT-4V as the annoator to assess the quality of each response regarding helpfulessn, visual faithfulness, and ethical considerations.
<br>
<br>
<div style="text-align: center;">
<img id="teaser" width="90%" src="./LLaVA_files/vdpov2.png">
</div>
<br>
<br>
(Left) The overall score distribution of three aspects. (Right) The comparison of models in our pool.
</centering>
<div style="text-align: center;">
<img id="teaser" width="90%" src="./LLaVA_files/scores_of_vlm.png">
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<!-- Results. -->
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<h2 class="title is-3"><img id="painting_icon" width="5%" src="./LLaVA_files/silkie.png"> Silkie: A Better Aligned LVLM </h2>
<!-- <h2 class="title is-3"><img id="painting_icon" width="3%" src="./LLaVA_files/silkie.png"> Silkie: A Better Aligned LVLM </h2> -->
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<!--/ Results. -->
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<p>
<!-- LLaVa-RLHF connects pre-trained <a href="https://openai.com/research/clip">CLIP ViT-L/14</a> visual encoder and large language model <a href="https://github.com/lm-sys/FastChat">Vicuna</a>, using a simple projection matrix and a LoRA module. We consider a three-stage alignment procedure: -->
We improve Qwen-VL-Chat by performing DPO on our VLFeedback, using the efficient LoRA tuning method. After DPO training, the resulting model Silkie achieves promising results compared with other models with similar-sized LLMs as the backbone.
<!-- </p><ul type="1">
<li><b>Stage 1: Supervised Fine-tuning.</b> <span style="font-size: 95%;"></span>
<ul type="1">
<li> Following LLaVA, we conduct pre-training for Feature Alignment. Only the projection matrix is updated, based on a subset of CC3M.
<li> Visual Chat and HQ Multimodal Instruction: LLaVA-SFT<sup>+</sup> is fine-tuned on 90k LLaVA-Instruct task, 83k VQA-v2 and 16k A-OKVQA multi-round QA task, and 23k Flickr30k Spotting Caption task.
</ul>
</li>
<li><b>Stage 2: Human Preference Collection & Preference Modeling.
</b> <span style="font-size: 95%;">
<ul type="1">
<li> We collect 10k human preferences where human annotators are asked to compare two responses and pinpoint the more hallucinated one.
</ul>
<li><b>Stage 3: Factually-Augmented RLHF</b>. <span style="font-size: 95%;">
<ul type="1">
<li> Only the LoRA module on top of LLaVA-SFT<sup>+</sup> is fine-tuned to get the Reward Model on 10k human preference data and the RL Model via reinforcement learning (PPO) from simulated human preferences.
</ul>
</span></li></ul> -->
<!-- Please check out our <a href="https://huggingface.co/zhiqings/LLaVA-RLHF-13b-v1.5-336">[LLaVA-RLHF-13bx336-v1.5]</a> model checkpoint. -->
<p></p>
</div>
<centering>
<div style="text-align: center;">
<img id="teaser" width="90%" src="./LLaVA_files/new_ret.png">
</div>
<br>
(Left) In-depth analysis on the MME benchmark for the performance improvements.
Our VLFeedback dataset brings clearer gains in OCR recognition and code reasoning tasks.
<br>
(Right) Relative performance improvement by performing DPO with RLHF-V preference data and a subset of our VLFeedback dataset. Our GPT-4V annotated preference dataset brings more consistent improvements on four benchmarks.
<br>
<br>
<div style="text-align: center;">
<img id="teaser" width="95%" src="./LLaVA_files/analysis.png">
</div>
</centering>
</div>
</div>
</div></section>
<section class="section">
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<h3 class="title is-3"> Qualitative Examples</h3>
</div>
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<!-- <h2 class="title is-4">Visual Reasoning on two examples from <a href="https://arxiv.org/abs/2303.08774">OpenAI GPT-4 Technical Report</a></h2> -->
</div>
</div>
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths">
Our Silkie locates the wooden stools with a red flower without giving misleading assertions (Left), and correctly answers the scientific-related question (Right), exhibiting better perception and cognition capabilities.
<img id="teaser" width="70%" src="./LLaVA_files/case_study1.png">
<br>
<br>
On a challenging query asking the model to generate a report for the diagram of weather forecast process, Silkie generates a well-structured report satisfying the word requirement.
<br>
<br>
<img id="teaser" width="70%" src="./LLaVA_files/case_study2.png">
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<!-- <p><b>Description:</b> Monalisa is a famous painting by Leonardo da Vinci. </p> -->
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<!-- <div class="chat-history"><article class="media"><figure class="media-left"><span class="icon is-large"><svg class="svg-inline--fa fa-user fa-w-14 fa-2x" aria-hidden="true" focusable="false" data-prefix="fas" data-icon="user" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" data-fa-i2svg=""><path fill="currentColor" d="M224 256c70.7 0 128-57.3 128-128S294.7 0 224 0 96 57.3 96 128s57.3 128 128 128zm89.6 32h-16.7c-22.2 10.2-46.9 16-72.9 16s-50.6-5.8-72.9-16h-16.7C60.2 288 0 348.2 0 422.4V464c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48v-41.6c0-74.2-60.2-134.4-134.4-134.4z"></path></svg><i class="fas fas fa-2x fa-user "></i> Font Awesome fontawesome.com</span></figure><div class="media-content"><div class="content"><p><strong>User</strong><br><pre style="background-color: white; font-size: 18px; font-family: Arial; padding: 0px; margin: 0px; white-space: pre-wrap; overflow-wrap: break-word;"></pre><img src="./LLaVA_files/monalisa.jpg" style="max-width: 100%; max-height: 300px;"></p></div></div></article><article class="media"><figure class="media-left"><span class="icon is-large"><svg class="svg-inline--fa fa-user fa-w-14 fa-2x" aria-hidden="true" focusable="false" data-prefix="fas" data-icon="user" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" data-fa-i2svg=""><path fill="currentColor" d="M224 256c70.7 0 128-57.3 128-128S294.7 0 224 0 96 57.3 96 128s57.3 128 128 128zm89.6 32h-16.7c-22.2 10.2-46.9 16-72.9 16s-50.6-5.8-72.9-16h-16.7C60.2 288 0 348.2 0 422.4V464c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48v-41.6c0-74.2-60.2-134.4-134.4-134.4z"></path></svg><i class="fas fas fa-2x fa-user "></i> Font Awesome fontawesome.com</span></figure><div class="media-content"><div class="content"><p><strong>User</strong><br><pre style="background-color: white; font-size: 18px; font-family: Arial; padding: 0px; margin: 0px; white-space: pre-wrap; overflow-wrap: break-word;">Do you know who drew this painting?</pre></p></div></div></article><article class="media"><figure class="media-left"><span class="icon is-large"><svg class="svg-inline--fa fa-robot fa-w-20 fa-2x" aria-hidden="true" focusable="false" data-prefix="fas" data-icon="robot" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 640 512" data-fa-i2svg=""><path fill="currentColor" d="M32,224H64V416H32A31.96166,31.96166,0,0,1,0,384V256A31.96166,31.96166,0,0,1,32,224Zm512-48V448a64.06328,64.06328,0,0,1-64,64H160a64.06328,64.06328,0,0,1-64-64V176a79.974,79.974,0,0,1,80-80H288V32a32,32,0,0,1,64,0V96H464A79.974,79.974,0,0,1,544,176ZM264,256a40,40,0,1,0-40,40A39.997,39.997,0,0,0,264,256Zm-8,128H192v32h64Zm96,0H288v32h64ZM456,256a40,40,0,1,0-40,40A39.997,39.997,0,0,0,456,256Zm-8,128H384v32h64ZM640,256V384a31.96166,31.96166,0,0,1-32,32H576V224h32A31.96166,31.96166,0,0,1,640,256Z"></path></svg><i class="fas fas fa-2x fa-robot"></i> Font Awesome fontawesome.com</span></figure><div class="media-content"><div class="content"><p><strong>LLaVA</strong><br><pre style="background-color: white; font-size: 18px; font-family: Arial; padding: 0px; margin: 0px; white-space: pre-wrap; overflow-wrap: break-word;">The painting depicts a woman, commonly believed to be Mona Lisa, the famous artwork by Leonardo da Vinci. It is a portrait painting that showcases the woman's enigmatic smile and has become one of the most famous and iconic art pieces in the world. The original work is displayed in the Louvre Museum in Paris, and it is known for its intricate details, use of oil paint, and the artist's innovative techniques that contributed to its enduring appeal and mystery.</pre></p></div></div></article></div> -->
<!-- </div> -->
<!-- </div> -->
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</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>
@inproceedings{li-etal-2024-vlfeedback,
title = "{VLF}eedback: A Large-Scale {AI} Feedback Dataset for Large Vision-Language Models Alignment",
author = "Li, Lei and
Xie, Zhihui and
Li, Mukai and
Chen, Shunian and
Wang, Peiyi and
Chen, Liang and
Yang, Yazheng and
Wang, Benyou and
Kong, Lingpeng and
Liu, Qi",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
year = "2024",
url = "https://aclanthology.org/2024.emnlp-main.358",
pages = "6227--6246"
}
</code></pre>
</div>
</section>
<section class="section" id="Acknowledgement">
<div class="container is-max-desktop content">
<h2 class="title">Acknowledgement</h2>
<p>
This website is adapted from <a href="https://github.com/nerfies/nerfies.github.io">Nerfies</a> and <a href="https://llava-rlhf.github.io/">LLaVA-RLHF</a>, licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
Commons Attribution-ShareAlike 4.0 International License</a>.
We thank the authors of the multi-modal instruction tuning datasets and open-source projects, including LLaVA, LLaVA-RLHF and Qwen-VL.
We would thank <a href="https://runxinxu.github.io/aboutme/">Runxin Xu</a> for his great help on the project.
</p>
<p>
<b>Usage and License Notices</b>: The data, code and checkpoint is intended and licensed for research use only. They are also restricted to uses that follow the license agreement of Qwen-VL and GPT-4. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.
</p>
<p>
<!-- <a href="https://github.com/Computer-Vision-in-the-Wild/"><img id="painting_icon" width="10%" src="./LLaVA_files/97258247"></a> -->
Related Links:
<a href="https://llava.hliu.cc/">[LLaVA] </a>
<a href="https://llava-rlhf.github.io/">[LLaVA-RLHF] </a>
<a href="https://github.com/QwenLM/Qwen-VL/">[Qwen-VL] </a>
</p>
</div>
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"It is a portrait painting that showcases the woman's enigmatic smile and has become one of the most famous and iconic art pieces in the world. " +
"The original work is displayed in the Louvre Museum in Paris, and it is known for its intricate details, use of oil paint, and the artist's innovative techniques that contributed to its enduring appeal and mystery."
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"description": "monalisa with dog head",
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["User", "Do you know who drew this painting?"],
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"description": "Titanic Rose and Jack",
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Ingredients:
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3. Sprinkle the chopped fresh parsley or mint over the fruit and gently toss once more.
4. If desired, add a drizzle of honey or maple syrup for a touch of sweetness. Adjust the amount according to your taste preferences.
5. Cover the bowl and refrigerate the fruit salad for at least 30 minutes or up to a few hours. This allows the flavors to meld and creates a more refreshing salad.
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One of the highlights of my trip was undoubtedly the fishing experience. The area is teeming with life, and I was thrilled to land a sizeable catch, despite the modest size of the lake. The joy of fishing in such a beautiful setting was truly rewarding, and it allowed me to take a break from my busy life and truly immerse myself in the tranquility of nature.
While exploring the surrounding area, I came across a family of birds that had built a nest close to the pier, adding to the charm of the location. Interacting with the wildlife and observing their behavior was an enriching experience that I'll cherish for years to come.
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