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122            🔥 More Research
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125            <a class="navbar-item" href="https://vl-rewardbench.github.io">
126              VL-RewardBench
127            </a>
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138             VLFeedback
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154            <!-- <h1 class="title is-1 publication-title">VLFeedback and Silkie</h1> -->
155            <!-- <h1 class="title is-1 publication-title">
156              <img src="./LLaVA_files/silkie.png" alt="VLFeedback and Silkie" style="height: 50px; width: 50px;"> VLFeedback and Silkie
157          </h1> -->
158          <h1 class="title is-1 publication-title">
159            <span style="display: inline-block; vertical-align: middle;">VLFeedback</span>
160            <span style="display: inline-block; vertical-align: middle; margin-top: -15px; margin-left: -7px;">
161                <img src="./LLaVA_files/silkie.png" alt="VLFeedback and Silkie" style="height: 50px; width: 50px;">
162            </span>
163        </h1>
164            <h3 class="title is-3 publication-title">A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment</h3>
165            <div class="is-size-5">
166              <span class="author-block">
167                <a href="https://lilei-nlp.github.io" style="color:#008AD7;font-weight:normal;">Lei Li<sup>*</sup></a>,
168              </span>
169              <span class="author-block">
170                <a href="https://zhxie.site/" style="color:#008AD7;font-weight:normal;">Zhihui Xie<sup>*</sup></a>,
171              </span>
172              <span class="author-block">
173                <a href="https://scholar.google.com/citations?user=BizedOAAAAAJ" style="color:#008AD7;font-weight:normal;">Mukai Li</a>,
174              </span> <br>
175              <span class="author-block">
176                <a href="https://github.com/Shunian-Chen" style="color:#F2A900;font-weight:normal;">Shunian Chen</a>,
177              </span>
178              <span class="author-block">
179                <a href="https://scholar.google.com.tw/citations?user=K0uQ3ygAAAAJ" style="color:#94070A;font-weight:normal;">Peiyi Wang</a>,
180              </span>
181              <span class="author-block">
182                <a href="https://chenllliang.github.io/about/" style="color:#94070A;font-weight:normal;">Liang Chen</a>,
183              </span> <br>
184              <span class="author-block">
185                <a href="https://scholar.google.com/citations?user=SAeMbW4AAAAJ&hl=en" style="color:#008AD7;font-weight:normal;">Yazheng Yang</a>,
186              </span>
187              <span class="author-block">
188                <a href="https://wabyking.github.io/old.html" style="color:#F2A900;font-weight:normal;">Benyou Wang</a>,
189              </span>
190              <span class="author-block">
191                <a href="https://ikekonglp.github.io/" style="color:#008AD7;font-weight:normal;">Lingpeng Kong</a>,
192              </span>
193              <span class="author-block">
194                <a href="https://leuchine.github.io/" style="color:#008AD7;font-weight:normal;">Qi Liu</a>
195              </span>
196
197              </span>
198            </div>
199
200            <br>
201            <div class="is-size-5 publication-authors">
202              <span class="author-block"><b style="color:#008AD7; font-weight:normal">▶ </b>The University of Hong Kong</span>
203              <br>
204              <span class="author-block"> <b style="color:#F2A900;  font-weight:normal"> ▶ </b> The Chinese University of Hong Kong, Shenzhen </span>  <br> 
205              <span class="author-block"><b style="color:#94070A; font-weight:normal">▶ </b>Peking University</span> <br>
206              <span class="author-block">&nbsp;&nbsp;<sup>*</sup>Equal Contribution</span>
207            </div>
208
209
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212                <span class="link-block">
213                  <a href="https://arxiv.org/abs/2410.09421" target="_blank" class="external-link button is-normal is-rounded is-dark">
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215                      <!-- <i class="ai-arxiv"></i> -->
216                    </span>
217                    <span>arXiv</span>
218                  </a>
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221                  <a href="https://github.com/vlf-silkie/VLFeedback" target="_blank" class="external-link button is-normal is-rounded is-dark">
222                    <span class="icon">
223                      <svg class="svg-inline--fa fa-github fa-w-16" aria-hidden="true" focusable="false" data-prefix="fab" data-icon="github" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512" data-fa-i2svg=""><path fill="currentColor" d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"></path></svg><!-- <i class="fab fa-github"></i> Font Awesome fontawesome.com -->
224                    </span>
225                    <span>Code</span>
226                  </a>
227                </span>
228                <!-- <span class="link-block">
229                  <a href="http://pitt.lti.cs.cmu.edu:7890/" target="_blank" class="external-link button is-normal is-rounded is-dark">
230                    <span class="icon">
231                      <svg class="svg-inline--fa fa-images fa-w-18" aria-hidden="true" focusable="false" data-prefix="far" data-icon="images" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" data-fa-i2svg=""><path fill="currentColor" d="M480 416v16c0 26.51-21.49 48-48 48H48c-26.51 0-48-21.49-48-48V176c0-26.51 21.49-48 48-48h16v48H54a6 6 0 0 0-6 6v244a6 6 0 0 0 6 6h372a6 6 0 0 0 6-6v-10h48zm42-336H150a6 6 0 0 0-6 6v244a6 6 0 0 0 6 6h372a6 6 0 0 0 6-6V86a6 6 0 0 0-6-6zm6-48c26.51 0 48 21.49 48 48v256c0 26.51-21.49 48-48 48H144c-26.51 0-48-21.49-48-48V80c0-26.51 21.49-48 48-48h384zM264 144c0 22.091-17.909 40-40 40s-40-17.909-40-40 17.909-40 40-40 40 17.909 40 40zm-72 96l39.515-39.515c4.686-4.686 12.284-4.686 16.971 0L288 240l103.515-103.515c4.686-4.686 12.284-4.686 16.971 0L480 208v80H192v-48z"></path></svg><!-- <i class="far fa-images"></i> Font Awesome fontawesome.com -->
232                    <!-- </span> -->
233                    <!-- <span>Demo</span> -->
234                  <!-- </a> -->
235                <!-- </span> -->
236                 <span class="link-block">
237                  <a href="https://huggingface.co/datasets/MMInstruction/VLFeedback" target="_blank" class="external-link button is-normal is-rounded is-dark">
238                    <span class="icon">
239                      <svg class="svg-inline--fa fa-database fa-w-14" aria-hidden="true" focusable="false" data-prefix="fas" data-icon="database" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" data-fa-i2svg=""><path fill="currentColor" d="M448 73.143v45.714C448 159.143 347.667 192 224 192S0 159.143 0 118.857V73.143C0 32.857 100.333 0 224 0s224 32.857 224 73.143zM448 176v102.857C448 319.143 347.667 352 224 352S0 319.143 0 278.857V176c48.125 33.143 136.208 48.572 224 48.572S399.874 209.143 448 176zm0 160v102.857C448 479.143 347.667 512 224 512S0 479.143 0 438.857V336c48.125 33.143 136.208 48.572 224 48.572S399.874 369.143 448 336z"></path></svg><!-- <i class="fas fa-database"></i> Font Awesome fontawesome.com -->
240                    </span>
241                    <span>Dataset (VLFeedback)</span>
242                  </a>
243                </span>
244
245
246                <span class="link-block">
247                  <a href="https://huggingface.co/MMInstruction/Silkie" target="_blank" class="external-link button is-normal is-rounded is-dark">
248                    <span class="icon">
249                      <svg class="svg-inline--fa fa-share-square fa-w-18" aria-hidden="true" focusable="false" data-prefix="fas" data-icon="share-square" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" data-fa-i2svg=""><path fill="currentColor" d="M568.482 177.448L424.479 313.433C409.3 327.768 384 317.14 384 295.985v-71.963c-144.575.97-205.566 35.113-164.775 171.353 4.483 14.973-12.846 26.567-25.006 17.33C155.252 383.105 120 326.488 120 269.339c0-143.937 117.599-172.5 264-173.312V24.012c0-21.174 25.317-31.768 40.479-17.448l144.003 135.988c10.02 9.463 10.028 25.425 0 34.896zM384 379.128V448H64V128h50.916a11.99 11.99 0 0 0 8.648-3.693c14.953-15.568 32.237-27.89 51.014-37.676C185.708 80.83 181.584 64 169.033 64H48C21.49 64 0 85.49 0 112v352c0 26.51 21.49 48 48 48h352c26.51 0 48-21.49 48-48v-88.806c0-8.288-8.197-14.066-16.011-11.302a71.83 71.83 0 0 1-34.189 3.377c-7.27-1.046-13.8 4.514-13.8 11.859z"></path></svg><!-- <i class="fas fa-share-square"></i> Font Awesome fontawesome.com -->
250                    </span>
251                    <span>Model</span>
252                  </a>
253                </span>
254
255
256                <!-- <span class="link-block">
257                <a href="#"
258                   class="external-link button is-normal is-rounded is-dark">
259                  <span class="icon">
260                    <i class="fab fa-youtube"></i>
261                  </span>
262                  <span>Video</span>
263                  </a>
264              </span> -->
265              </div>
266            </div>
267          </div>
268        </div>
269      </div>
270    </div>
271  </section>
272
273  <section class="hero teaser">
274    <div class="container is-max-desktop">
275      <div class="hero-body">
276        
277        <h4 class="subtitle has-text-centered" style="color:rgb(0, 4, 255)">
278          Accepted by EMNLP 2024 Main Conference
279        </h4>
280        <h4 class="subtitle has-text-centered">
281          <!-- 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> -->
282          <!-- 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> -->
283          <!-- 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. -->
284          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>
285          Based on Qwen-VL-Chat, we present Silkie, by performing DPO on our VLFeedback. Compared with the original model, Silkile 
286          <strong>achieves 6.9% and 9.5% relative improvement on the MME benchmark regarding the perception and cognition capabilities</strong>, respectively. 
287          Besides, Silkie sets a new <strong>state-of-the-art score of 3.02 on MMHal-Bench</strong> regarding hallucination evaluation.
288        </h4>
289      </div>
290    </div>
291  </section>
292
293
294
295  <section class="section" style="background-color:#efeff081">
296    <div class="container is-max-desktop">
297      <!-- Abstract. -->
298      <div class="columns is-centered has-text-centered">
299        <div class="column is-six-fifths">
300          <h2 class="title is-3">Abstract</h2>
301          <div class="content has-text-justified">
302            <p>
303              As large vision-language models (LVLMs) evolve rapidly, the demand for high-quality and diverse data to align these models becomes increasingly crucial.
304              However, the creation of such data with human supervision proves costly and time-intensive.
305              In this paper, we investigate the efficacy of AI feedback to scale supervision for aligning LVLMs.
306              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.
307              To evaluate the effectiveness of AI feedback for vision-language alignment, we train Silkie, an LVLM fine-tuned via direct preference optimization on VLFeedback.
308              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.
309              Furthermore, our analysis underscores the advantage of AI feedback, particularly in fostering preference diversity to deliver more comprehensive improvements.
310           <p></p>
311  
312          </div>
313        </div>
314      </div>
315        
316    </div>
317  </section>
318
319
320  
321<section class="section">
322  <!-- Results. -->
323  <div class="columns is-centered has-text-centered">
324    <div class="column is-six-fifths">
325      <h2 class="title is-3"><img id="painting_icon" width="3%" src="./LLaVA_files/5886212.png"> Multimodal Instructions and AI Preference Data</h2>
326    </div>
327  </div>
328  <!-- </div> -->
329  <!--/ Results. -->    
330<div class="container is-max-desktop">
331
332  <div class="columns is-centered">
333    <div class="column is-full-width">
334      <div class="content has-text-justified">
335        <p>
336          We sample multi-modal instructions from various souces, covering different capabilities of LVLMs. We further build a model pool consisting of 12 LVLMs. </a>.
337          <centering>
338            <div style="text-align: center;">
339              <img id="teaser" width="80%" src="./LLaVA_files/instruction_source.png">     
340            </div> 
341            <br>
342          We further use GPT-4V as the annoator to assess the quality of each response regarding helpfulessn, visual faithfulness, and ethical considerations. 
343          <br>  
344          <br>
345
346          <div style="text-align: center;">
347              <img id="teaser" width="90%" src="./LLaVA_files/vdpov2.png">     
348            </div>
349            <br>
350            <br>
351          (Left) The overall score distribution of three aspects. (Right) The comparison of models in our pool.
352          </centering>          
353          <div style="text-align: center;">
354            <img id="teaser" width="90%" src="./LLaVA_files/scores_of_vlm.png">     
355          </div> 
356    </div>
357  </div>
358
359
360</div></section>
361 
362
363<section class="section">
364  <!-- Results. -->
365  <div class="columns is-centered has-text-centered">
366    <div class="column is-six-fifths">
367      <h2 class="title is-3"><img id="painting_icon" width="5%" src="./LLaVA_files/silkie.png"> Silkie: A Better Aligned LVLM </h2>
368      <!-- <h2 class="title is-3"><img id="painting_icon" width="3%" src="./LLaVA_files/silkie.png"> Silkie: A Better Aligned LVLM </h2> -->
369    </div>
370  </div>
371  <!-- </div> -->
372  <!--/ Results. -->    
373<div class="container is-max-desktop">
374
375  <div class="columns is-centered">
376    <div class="column is-full-width">
377      <div class="content has-text-justified"> 
378        <p>
379          <!-- 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: -->
380           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.
381          <!-- </p><ul type="1">
382            <li><b>Stage 1: Supervised Fine-tuning.</b> <span style="font-size: 95%;"></span>
383            <ul type="1">
384              <li> Following LLaVA, we conduct pre-training for Feature Alignment. Only the projection matrix is updated, based on a subset of CC3M.
385                <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.
386              </ul>
387            </li>
388            <li><b>Stage 2: Human Preference Collection & Preference Modeling. 
389            </b> <span style="font-size: 95%;">
390              <ul type="1">
391                <li> We collect 10k human preferences where human annotators are asked to compare two responses and pinpoint the more hallucinated one.
392              </ul>  
393            <li><b>Stage 3: Factually-Augmented RLHF</b>. <span style="font-size: 95%;">
394              <ul type="1">
395                <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.
396              </ul>
397          </span></li></ul>   -->
398          <!-- Please check out our <a href="https://huggingface.co/zhiqings/LLaVA-RLHF-13b-v1.5-336">[LLaVA-RLHF-13bx336-v1.5]</a> model checkpoint. -->
399        <p></p>
400      </div>
401      <centering>
402        <div style="text-align: center;">
403          <img id="teaser" width="90%" src="./LLaVA_files/new_ret.png">     
404        </div>
405        <br>
406
407        (Left) In-depth analysis on the MME benchmark for the performance improvements. 
408        Our VLFeedback dataset brings clearer gains in OCR recognition and code reasoning tasks. 
409        <br>
410        
411        (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.
412        <br>
413        <br>
414        <div style="text-align: center;">
415          <img id="teaser" width="95%" src="./LLaVA_files/analysis.png">     
416        </div>
417      </centering>           
418    </div>
419  </div>
420
421
422</div></section>
423  
424
425
426
427
428<section class="section">
429
430  <div class="columns is-centered has-text-centered">
431    <div class="column is-six-fifths">
432      <h3 class="title is-3"> Qualitative Examples</h3>
433    </div>
434  </div>
435
436    <div class="columns is-centered has-text-centered">
437      <div class="column is-six-fifths">
438         <!-- <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> -->
439      </div>
440      </div>  
441      
442    <div class="columns is-centered has-text-centered">
443      
444    <div class="column is-six-fifths">
445      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.
446      <img id="teaser" width="70%" src="./LLaVA_files/case_study1.png">
447      <br>
448      <br>
449      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.
450      <br>
451      <br>
452    
453
454      <img id="teaser" width="70%" src="./LLaVA_files/case_study2.png">
455
456    </div>
457    </div>  
458
459  
460
461  
462
463  
464
465
466  <div class="container mt-5">
467    <!-- <h2 class="text-center mb-5">Who's GPT-4's favorite? Battles between State-of-the-Art Chatbots</h2> -->
468    <!-- Selection -->
469    <!-- <div class="form-row" style="justify-content: flex-end;">
470      <div class="form-group col-md-1">
471        <div class="col-md-2" style="width: 100%"><label>&nbsp;</label></div>
472        <div class="btn-group" role="group" aria-label="Left and Right Controller" style="width: 100%;align-items: flex-end;justify-content: center;flex-direction: row;display: flex;">
473          <button type="button" class="form-control btn btn-primary" id="prev-question"><i class="material-icons">keyboard_arrow_left</i></button>
474          <button type="button" class="form-control btn btn-primary" id="next-question"><i class="material-icons">keyboard_arrow_right</i></button>
475
476        </div>
477      </div>
478    </div> -->
479
480    <!-- Question Card -->
481    <div style="display: flex; justify-content: center; align-items: center;">
482      <div class="card mb-4" style="width: 100%; display: flex; align-items: center;">
483        <!-- <p><b>Description:</b> Monalisa is a famous painting by Leonardo da Vinci. </p> -->
484
485        <!-- <div class="card-body" id="selected-question" style="display: flex; height: 80vh;"> -->
486          <!-- <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="">
486<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> -->
487
488        <!-- </div> -->
489      <!-- </div> -->
490    <!-- </div> -->
491
492  </div>
493</section>
494
495  <section class="section" id="BibTeX">
496    <div class="container is-max-desktop content">
497      <h2 class="title">BibTeX</h2>
498      <pre><code>
499@inproceedings{li-etal-2024-vlfeedback,
500    title = "{VLF}eedback: A Large-Scale {AI} Feedback Dataset for Large Vision-Language Models Alignment",
501    author = "Li, Lei  and
502      Xie, Zhihui  and
503      Li, Mukai  and
504      Chen, Shunian  and
505      Wang, Peiyi  and
506      Chen, Liang  and
507      Yang, Yazheng  and
508      Wang, Benyou  and
509      Kong, Lingpeng  and
510      Liu, Qi",
511    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
512    year = "2024",
513    url = "https://aclanthology.org/2024.emnlp-main.358",
514    pages = "6227--6246"
515}
516  </code></pre>
517    </div>
518  </section>
519  
520  <section class="section" id="Acknowledgement">
521    <div class="container is-max-desktop content">
522      <h2 class="title">Acknowledgement</h2>
523      <p>
524        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
525        Commons Attribution-ShareAlike 4.0 International License</a>.  
526        We thank the authors of the multi-modal instruction tuning datasets and open-source projects, including LLaVA, LLaVA-RLHF and Qwen-VL. 
527        We would thank <a href="https://runxinxu.github.io/aboutme/">Runxin Xu</a> for his great help on the project.
528      </p>
529
530      <p>
531<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.
532</p>
533
534      <p>
535      <!-- <a href="https://github.com/Computer-Vision-in-the-Wild/"><img id="painting_icon" width="10%" src="./LLaVA_files/97258247"></a>  -->
536      Related Links: 
537      <a href="https://llava.hliu.cc/">[LLaVA] </a>     
538      <a href="https://llava-rlhf.github.io/">[LLaVA-RLHF] </a>      
539      <a href="https://github.com/QwenLM/Qwen-VL/">[Qwen-VL] </a>      
540
541      </p>    
542    </div>
543  </section>
544
545  
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616        "description": "monalisa",
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620          ["User", "Do you know who drew this painting?​"],
621          ["LLaVA",
622            "The painting depicts a woman, commonly believed to be Mona Lisa, the famous artwork by Leonardo da Vinci. " +
623            "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. " +
624            "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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628      {
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632          ["User", "Do you know who drew this painting?​"],
633          ["LLaVA", "The painting depicts a dog in a humorous situation, where it appears to be wearing a woman's dress and standing in front of a landscape. Although the artist of the painting is not explicitly identified, it could be a reference to the famous artist Da Vinci, known for his artistic works, including the Mona Lisa. The painting is likely a creative and comical take on the traditional portrait style, replacing a human figure with a dog dressed in a woman's clothing, making it an interesting and amusing visual piece.​"]
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636      {
637        "description": "Titanic Rose and Jack",
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640          ["User", "What's the ending of this movie?​​"],
641          ["LLaVA", "​In the image, a young woman is leaning on a man in front of a beautiful sunset, with both of them standing on a boat. They appear to be enjoying a romantic moment together. The scene suggests that they could be depicted as the characters Jack and Rose from the popular movie Titanic. In the mov
641ie, their romance blossoms aboard the fictional ocean liner Titanic, with the pair ultimately sharing a passionate kiss as the ship sinks into the ocean. The ending of the movie is known for its emotional and heartfelt scenes, where the characters are forced to say goodbye to each other as they are separated by the sinking ship. The image evokes a sense of romance and nostalgia, paying tribute to the iconic love story portrayed in the movie Titanic.​"]
642        ]
643      },
644      {
645        "description": "Fridge",
646        "turns": [
647          ["User", "", "/static/images/fridge.jpg"],
648          ["User", "What are the meals that I can cook with these?​​"],
649          ["LLaVA", "With the variety of food items stored in the refrigerator, you can prepare several meals. For example, you can create a fruit salad using the strawberries, blueberries, and carrots. Additionally, you can make a delicious smoothie using the fruits and vegetables, which would be a healthy and nutritious snack or breakfast option. The milk and eggs can be used to create various dishes, such as a milk-based smoothie, a baked casserole, or scrambled eggs. The juice can also be used to make a fresh fruit and juice mix or as a refreshing homemade popsicle. The possibilities are vast, and the meals you can create will depend on your preferences and dietary needs.​​"],
650          ["User", "I would like to do the fruit salad.  Show me the recipe.​"],
651          ["LLaVA",
652            `Certainly! Here's a simple fruit salad recipe using the items in your refrigerator:​
653
654Ingredients:​
655- 2 cups fresh strawberries, sliced​
656- 1 cup fresh blueberries​
657- 1 large carrot, grated​
658- Juice of half a lemon​
659- A handful of chopped fresh parsley or mint​
660- A few ice cubes​
661- Optional: honey or maple syrup (adjust to taste)​
662
663Instructions:​
6641. In a large bowl, combine the sliced strawberries, blueberries, and carrot. Gently toss them together to mix.​
6652. Drizzle the juice of half a lemon over the fruit and vegetables to prevent them from oxidizing and add a refreshing citrus flavor.​
6663. Sprinkle the chopped fresh parsley or mint over the fruit and gently toss once more.​
6674. If desired, add a drizzle of honey or maple syrup for a touch of sweetness. Adjust the amount according to your taste preferences.​
6685. 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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670
671This fruit salad recipe is perfect for a healthy snack or side dish, and with the variety of ingredients available in your refrigerator, it should be easy to put together quickly.`]]
672      },
673      {
674        "description": "Beautiful View",
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677          ["User", "What are the things I should be cautious about when I visit here?​​"],
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679          ["User", "Compose an engaging travel blog post about my trip to this place, highlighting the beautiful sceneries, joyful experiences, and include possible anecdotes.​​"],
680          ["LLaVA",
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682
683One 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.​
684
685While 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.​
686
687In conclusion, this idyllic destination provided me with an opportunity to unwind and recharge, surrounded by mesmerizing natural beauty and the gentle sounds of water and wildlife. If you're looking for a serene escape from the hustle and bustle of everyday life, this pier overlooking the lake is the perfect place to find solace and reconnect with nature.`
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