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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>WangLab at MEDIQA-CORR 2024: Optimized LLM-based Programs for Medical Error Detection and Correction · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2404.14544/</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/2404.14544/feed.xml" rel="self" type="application/rss+xml" /><item><title>WangLab at MEDIQA-CORR 2024: Optimized LLM-based Programs for Medical Error Detection and Correction — mentioned by choppaface</title><link>https://news.ycombinator.com/item?id=42347675</link><guid isPermaLink="false">urn:hackerpapers:mention:2404.14544:42347675</guid><pubDate>Sat, 07 Dec 2024 06:21:18 GMT</pubDate><description>&lt;p&gt;The main idea behind DSPy is that you can’t modify the weights, but you can perhaps modify the prompts. DSPy’s original primary customer was multi-llm-agent systems where you have a chain / graph of LLM calls (perhaps mostly or all to OpenAI GPT) and you have some metric (perhaps vague) that you want to increase. While the idea may seem a bit weird, there have been various success stories, such as a UoT team winning medical-notes-oriented competition using DSPy https://arxiv.org/html/2404.14544v1&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2404.14544/"&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>
