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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Finding Diamonds in Conversation Haystacks: A Benchmark for Conversational Data Retrieval · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2510.02938/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2510.02938/feed.xml" rel="self" type="application/rss+xml" /><item><title>Finding Diamonds in Conversation Haystacks: A Benchmark for Conversational Data Retrieval — mentioned by Kappa90</title><link>https://news.ycombinator.com/item?id=47678256</link><guid isPermaLink="false">urn:hackerpapers:mention:2510.02938:47678256</guid><pubDate>Tue, 07 Apr 2026 17:00:14 GMT</pubDate><description>&lt;p&gt;It&amp;#x27;s an experimental benchmark, I couldn&amp;#x27;t find any off-the-shelf benchmarks to use this with. There&amp;#x27;s Spider 2.0 but it&amp;#x27;s for text-to-SQL. I&amp;#x27;m planning to run this [1] next but it&amp;#x27;s quite expensive. There&amp;#x27;s 75 questions, divided in 5 use case groups: revenue ops, e-commerce, knowledge bases, devops, support. I then generated a synthetic dataset with data mimicking APIs ranging from Stripe to Hubspot to Shopify to Zendesk etc.. I expose all the data through Dinobase vs. having one MCP per source e.g. one MCP for Stripe data, one MCP for Hubspot data etc. I tested this with 11 models, ranging…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2510.02938/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
