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<!DOCTYPE html>
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<title>RAG Meets LLMs: Towards Retrieval-Augmented Large Language Models</title>
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<div class="intro-heading text-uppercase">RAG Meets LLMs: Towards Retrieval-Augmented
Large Language Models</div>
<div class="intro-lead-in">RAG-Meets-LLMs Tutorial at <a href="https://advanced-recommender-systems.github.io/RAG-Meets-LLMs/">KDD'24</a></div>
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<h2 class="section-heading text-uppercase">About</h2>
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<p style="text-align:justify">
As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) techniques can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tasks. Particularly in the era of AI-generated content (AIGC), the powerful capacity of retrieval in RAG in providing additional knowledge enables retrieval-augmented generation to assist existing generative AI in producing high-quality outputs. Recently, large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation, while still facing inherent limitations, such as hallucinations and out-of-date internal knowledge. Given the powerful abilities of RAG in providing the latest and helpful auxiliary information, retrieval-augmented large language models have emerged to harness external and authoritative knowledge bases, rather than solely relying on the model's internal knowledge, to augment the generation quality of LLMs.
</p>
<p style="text-align:justify">
In this tutorial, we comprehensively review existing research studies in retrieval-augmented large language models (<b>RA-LLMs</b>), covering three primary technical perspectives: architectures, training strategies, and applications. As the preliminary knowledge, we briefly introduce the foundations and recent advances of LLMs. Then, to illustrate the practical significance of RAG for LLMs, we categorize mainstream relevant work by application areas, detailing the challenges of each and the corresponding capabilities of RA-LLMs specifically. Finally, to deliver deeper insights, we discuss current limitations and several promising directions for future research.
</p>
<p style="text-align:justify">Our Survey Paper: <a href="https://arxiv.org/abs/2405.06211">RAG-Meets-LLMs: Towards Retrieval-Augmented Large Language Models</a></p>
<p style="text-align:justify">Slides: <a href="RA-LLMs (KDD Tutorial)-823.pdf">Our Slides</a></p>
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<h2 class="section-heading text-uppercase">TARGET AUDIENCE AND PREREQUISITES FOR THE TUTORIAL</h2>
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The audience of this tutorial could be college students, researchers in academic institutions, and industrial AI labs who are interested in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). The audience is expected to have basic knowledge of artificial intelligence, language models, and retrieval techniques. However, this tutorial will be presented at the college junior/senior level so that it can be comfortably followed by academic researchers or industrial practitioners who are interested in this emerging field but not quite familiar with it. After attending this tutorial, the audience is expected to have a comprehensive understanding of Retrieval-Augmented Large Language Models and learn how to design a solution for a customized problem. </p>
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<h2 class="section-heading text-uppercase">Event Dates</h2>
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<b> Sunday, August 25 during 10 am - 1 pm (Barcelona Time)</b></p>
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<h2 class="section-heading text-uppercase">Tutorial Syllabus</h2>
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<!-- <b>The topics of this half-day tutorial include (but are not limited to) the following:</b></p>
<li style="color:black;">Introduction of Retrieval Augmented Large Language Models (RA-LLMs) </li>
<li style="color:black;">Architecture of RA-LLMs and Main Modules </li>
<li style="color:black;">Learning Approach of RA-LLMs </li>
<li style="color:black;">Applications of RA-LLMs </li>
<li style="color:black;">Challenges and Future Directions of RA-LLMs </li> -->
<!-- 标题字体调大 -->
<p style="font-size: 20px;"><b>The topics of this tutorial include (but are not limited to) the following:</b></p>
<li><b>Retrieval-Augmented Generation (RAG)</b></li>
<li><b>Large Language Model (LLM)</b></li>
<li><b>Pre-training</b></li>
<li><b>Fine-tuning</b></li>
<li><b>In-context Learning</b></li>
<li><b>Prompting</b></li>
<ul> </ul>
<p style="font-size: 20px;"><b>The tutorial outline is shown below:</b></p>
<!-- <b>The tutorial outline is shown below:</b></p> -->
<!-- <ul> -->
<li><b>Introduction of RA-LLMs (15 minutes)</b></li>
<ul> </ul>
<li><b>Architecture of RA-LLMs and Main Modules (30 minutes)</b></li>
<ul>
<li>RA-LLM architecture overview</li>
<li>Retriever in RA-LLMs</li>
<li>Retrieval results integration</li>
<li>Pre/Post-retrieval techniques</li>
<li>Special RA-LLM paradigms</li>
</ul>
<li><b>Coffee Break (20 minutes)</b></li>
<ul> </ul>
<li><b>Learning Approach of RA-LLMs (30 minutes)</b></li>
<ul>
<li>Training-free methods</li>
<li>Training-based Methods</li>
</ul>
<li><b>Applications of RA-LLMs (30 minutes)</b></li>
<ul>
<li>NLP application</li>
<li>Downstream tasks</li>
<li>Domain-specific applications</li>
</ul>
<li><b>Challenges and Future Directions of RA-LLMs (15 minutes)</b></li>
<ul>
<li>Trustworthy LLMs/RAG/RA-LLMs</li>
<li>Multi-modal RA-LLMs</li>
<li>Quality of external knowledge</li>
<li>Mamba-based RA-LLMs</li>
</ul>
<li><b>Q&A (10 minutes)</b></li>
</li>
<!-- </ul> -->
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<h3 class="section-heading text-uppercase">Tutorial TUTORS</h3>
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<a href="https://wenqifan03.github.io/"><img style="max-width:none" class="mx-auto" src="img/Wenqi.png" alt=""></a> <!-- rounded-circle-->
<h4><a href="https://wenqifan03.github.io/">Wenqi Fan</a></h4>
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<p class="text-muted">The Hong Kong Polytechnic University (PolyU)</p>
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<a href="https://joanding.github.io/"><img style="max-width:none" class="mx-auto" src="img/yujuan.png" alt=""></a>
<h4><a href="https://joanding.github.io/">Yujuan Ding</a></h4>
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<p class="text-muted">The Hong Kong Polytechnic University</p>
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<h4><a href="https://sjay-wang.github.io/">Shijie Wang</a></h4>
<p class="text-muted" style="display:inline">PhD Candidate</p>
<p class="text-muted">The Hong Kong Polytechnic University</p>
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<a href="https://biglemon-ning.github.io/"><img style="max-width:none" class="mx-auto" src="img/liangbo.png" alt=""></a>
<h4><a href="https://biglemon-ning.github.io/">Liangbo Ning</a></h4>
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<p class="text-muted">The Hong Kong Polytechnic University</p>
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<h4><a href="https://www.polyu.edu.hk/shtm/people/academic-staff/neil-li/">Hengyun Li</a></h4>
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<a href="https://www.yindawei.com/"><img style="max-width:none" class="mx-auto" src="img/dawei.png" alt=""></a>
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<p class="text-muted">Baidu inc.</p>
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<h4><a href="https://www.comp.nus.edu.sg/cs/people/chuats/">Tat-Seng Chua</a></h4>
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<h4><a href="https://www4.comp.polyu.edu.hk/~csqli/">Qing Li</a></h4>
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<script src="js/agency.min.js"></script>
</body>
</html>