<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>davedx</author><text>This seems highly relevant: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;abs&amp;#x2F;2406.01506&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;abs&amp;#x2F;2406.01506&lt;/a&gt;&lt;p&gt;&amp;gt; In this paper, we study the two foundational questions in this area. First, how are categorical concepts, such as {&amp;#x27;mammal&amp;#x27;, &amp;#x27;bird&amp;#x27;, &amp;#x27;reptile&amp;#x27;, &amp;#x27;fish&amp;#x27;}, represented? Second, how are hierarchical relations between concepts encoded? For example, how is the fact that &amp;#x27;dog&amp;#x27; is a kind of &amp;#x27;mammal&amp;#x27; encoded? We show how to extend the linear representation hypothesis to answer these questions. We find a remarkably simple structure: simple categorical concepts are represented as simplices, hierarchically related concepts are orthogonal in a sense we make precise, and (in consequence) complex concepts are represented as polytopes constructed from direct sums of simplices, reflecting the hierarchical structure.&lt;p&gt;Basically, LLM&amp;#x27;s &lt;i&gt;already partially encode information as semantic graphs internally&lt;/i&gt;.&lt;p&gt;With this it is less surprising that augmenting them with external knowledge graphs has a lower ROI.</text><parent_chain></parent_chain></comment><story><title>Knowledge Graphs in RAG: Hype vs. Ragas Analysis</title><url>https://aiencoder.substack.com/p/graphrag-analysis-part-1-how-indexing</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>visarga</author><text>The Microsoft GraphRAG paper focuses on global sensemaking through hierarchical summarization, which is a fundamental aspect of their approach. The blog post analysis, however, doesn&amp;#x27;t address this core feature at all. Another issue is the corpus size, the paper focuses on sizes on the order of 1M tokens, while the reference text used in the blog post is probably shorter. On shorter text a simple LLM call could do summarization directly.</text><parent_chain></parent_chain></comment><story><title>Knowledge Graphs in RAG: Hype vs. Ragas Analysis</title><url>https://aiencoder.substack.com/p/graphrag-analysis-part-1-how-indexing</url></story>