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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Can Foundation Models Wrangle Your Data? · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2205.09911/</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/2205.09911/feed.xml" rel="self" type="application/rss+xml" /><item><title>Can Foundation Models Wrangle Your Data? — mentioned by cccybernetic</title><link>https://news.ycombinator.com/item?id=37918641</link><guid isPermaLink="false">urn:hackerpapers:mention:2205.09911:37918641</guid><pubDate>Tue, 17 Oct 2023 17:39:47 GMT</pubDate><description>&lt;p&gt;A blog on using using LLMs to clean, process, and enrich data. It includes prompts and code snippets. The post draws on my experiences and two really interesting papers: - Can Foundation Models Wrangle Your Data? (https://arxiv.org/abs/2205.09911) - Large Language Models as Data Preprocessors (https://arxiv.org/abs/2308.16361) I cover: - Error and Anomaly Detection - Enriching Data with LLMs - Matching Data Labels - Identifying Matching Records Thank you and I&amp;#x27;d appreciate your feedback.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2205.09911/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
