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</h3><article class="archive-item">
<a href="/en/the-flow-of-graphrag/" class="archive-item-link">The Flow of GraphRAG</a>
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<a href="/en/reading-notes-mixture-of-experts/" class="archive-item-link">Reading Notes: Outrageously Large Neural Networks-The Sparsely-Gated Mixture-of-Experts Layer</a>
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<a href="/en/what-is-k-nearest-neighbor-algorithms/" class="archive-item-link">How KNN Algorithm Works</a>
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<a href="/en/llm-inference-optimization-kv-cache/" class="archive-item-link">LLM inference optimization - KV Cache</a>
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<a href="/en/lora-finetuning/" class="archive-item-link">LoRA fine-tuning</a>
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<a href="/en/a-trick-to-calculating-partial-derivatives-in-ml/" class="archive-item-link">A trick to calculating partial derivatives in machine learning</a>
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<title>Categories - Category - MartinLwx&#39;s Blog</title>
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</div><main class="main">
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<a href="/en/the-flow-of-graphrag/" class="archive-item-link">The Flow of GraphRAG</a>
<span class="archive-item-date">02-12</span>
</article><article class="archive-item">
<a href="/en/reading-notes-mixture-of-experts/" class="archive-item-link">Reading Notes: Outrageously Large Neural Networks-The Sparsely-Gated Mixture-of-Experts Layer</a>
<span class="archive-item-date">02-02</span>
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<title>ML-DL - Category - MartinLwx&#39;s Blog</title>
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<title>The Flow of GraphRAG</title>
<link>https://martinlwx.github.io/en/the-flow-of-graphrag/</link>
<pubDate>Wed, 12 Feb 2025 00:05:27 &#43;0800</pubDate><author>
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<description><![CDATA[<h2 id="motivation" class="headerLink">
<a href="#motivation" class="header-mark" aria-label="Header mark for 'Motivation'"></a>Motivation</h2><p>The current RAG techniques can not answer the <em>global questions</em> about the corpus. For example, we may want to know <em>what is the topic of the corpus</em>. Usually, the answer does not exist in the corpus but needs to understand <em>the whole corpus</em> and give summarization. Such global questions are called query-focused summarization (QFS) problems in this paper<sup id="fnref:1"><a href="#fn:1" class="footnote-ref" role="doc-noteref">1</a></sup>. A naive RAG technique can not handle such a situation.</p>]]></description>
</item><item>
<title>Reading Notes: Outrageously Large Neural Networks-The Sparsely-Gated Mixture-of-Experts Layer</title>
<link>https://martinlwx.github.io/en/reading-notes-mixture-of-experts/</link>
<pubDate>Sun, 02 Feb 2025 14:22:57 &#43;0800</pubDate><author>
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<a href="/en/the-flow-of-graphrag/">The Flow of GraphRAG</a>
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</span>&nbsp;<span class="post-publish">published on <time datetime="2025-02-12">2025-02-12</time></span>&nbsp;<span class="post-category">included in </span>&nbsp;<span class="post-category">category <a href="/en/categories/ml-dl/"><svg class="icon"
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<a href="#motivation" class="header-mark" aria-label="Header mark for 'Motivation'"></a>Motivation</h2><p>The current RAG techniques can not answer the <em>global questions</em> about the corpus. For example, we may want to know <em>what is the topic of the corpus</em>. Usually, the answer does not exist in the corpus but needs to understand <em>the whole corpus</em> and give summarization. Such global questions are called query-focused summarization (QFS) problems in this paper<sup id="fnref:1"><a href="#fn:1" class="footnote-ref" role="doc-noteref">1</a></sup>. A naive RAG technique can not handle such a situation.</p></div><div class="post-footer">
<a href="/en/the-flow-of-graphrag/">Read more...</a><div class="post-tags"><svg class="icon"
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</article>
<article class="single summary"><h1 class="single-title">
<a href="/en/reading-notes-mixture-of-experts/">Reading Notes: Outrageously Large Neural Networks-The Sparsely-Gated Mixture-of-Experts Layer</a>
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<article class="single summary"><h1 class="single-title">
<a href="/en/learn-to-use-dataclass-in-python/">Learn to Use @dataclass in Python</a>
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</span>&nbsp;<span class="post-publish">published on <time datetime="2024-08-17">2024-08-17</time></span>&nbsp;<span class="post-category">included in </span>&nbsp;<span class="post-category">category <a href="/en/categories/programming-languages/"><svg class="icon"
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<a href="#intro" class="header-mark" aria-label="Header mark for 'Intro'"></a>Intro</h2><p>I like Python&rsquo;s tuple, which allows us to quickly <em>bundle</em> together values of <em>different types</em> as a single entity and manage them in an intuitive and easy-to-use way. However, I find that once the tuple has <em>many fields</em>, I&rsquo;m <em>forced to</em> add a comment to indicate the meaning of each field. For example,</p>
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<p class="tw-select-none !tw-my-1">python</p></div><div class="post-footer">
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