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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Atlas: Few-shot Learning with Retrieval Augmented Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2208.03299/</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/2208.03299/feed.xml" rel="self" type="application/rss+xml" /><item><title>Atlas: Few-shot Learning with Retrieval Augmented Language Models — mentioned by mpsk</title><link>https://news.ycombinator.com/item?id=36842360</link><guid isPermaLink="false">urn:hackerpapers:mention:2208.03299:36842360</guid><pubDate>Mon, 24 Jul 2023 02:20:28 GMT</pubDate><description>&lt;p&gt;I am the author of this article. And actually what we tried to do was to replicate the simplest implementation to Retrieval Augmented Language Models by prompting the LLM. There have been many researches on this topic right now like work from Meta(https://arxiv.org/pdf/2208.03299v3.pdf). I think it can give you a picture how those RALMs boost the performance on General QA tasks.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2208.03299/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item><item><title>Atlas: Few-shot Learning with Retrieval Augmented Language Models — mentioned by ricopags</title><link>https://news.ycombinator.com/item?id=33993675</link><guid isPermaLink="false">urn:hackerpapers:mention:2208.03299:33993675</guid><pubDate>Thu, 15 Dec 2022 01:20:28 GMT</pubDate><description>&lt;p&gt;Or Meta&amp;#x27;s Atlas[0] for more recent work [0]https://arxiv.org/abs/2208.03299&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2208.03299/"&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>
