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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Reducing Selection Bias in Large Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2402.01740/</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/2402.01740/feed.xml" rel="self" type="application/rss+xml" /><item><title>Reducing Selection Bias in Large Language Models — mentioned by bluewin</title><link>https://news.ycombinator.com/item?id=39386824</link><guid isPermaLink="false">urn:hackerpapers:mention:2402.01740:39386824</guid><pubDate>Thu, 15 Feb 2024 18:57:44 GMT</pubDate><description>&lt;p&gt;I found guardrails AI really useful for my research on LLMs. Otherwise I would have wasted a lot of time trying to curate the outputs from my experiments. Don’t cram too much in a single prompt though. Prompt structures like guard rails naturally carry high cognitive load for the LLM, which leads to biased outputs. I found the best practice is to alleviate it by using multiple prompts and using a guard rail as an end-step rather than one big prompt for the LLM. (https://arxiv.org/abs/2402.01740)&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2402.01740/"&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>
