<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>PromptBench: A Unified Library for Evaluation of Large Language Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2312.07910/</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/2312.07910/feed.xml" rel="self" type="application/rss+xml" /><item><title>PromptBench: A Unified Library for Evaluation of Large Language Models — mentioned by Flux159</title><link>https://news.ycombinator.com/item?id=38764807</link><guid isPermaLink="false">urn:hackerpapers:mention:2312.07910:38764807</guid><pubDate>Mon, 25 Dec 2023 18:25:06 GMT</pubDate><description>&lt;p&gt;Some results published here https://llm-eval.github.io/pages/leaderboard/advprompt.html Also, technical report on arxiv https://arxiv.org/abs/2312.07910 I think that something that would make the results better would be highlighting which models perform best for specific tests (ie color coding) and explaining the tests via some hover info. Also with some fine tuned models training to get higher scores on specific tests, I don&amp;#x27;t know how valuable these tests are in comparison to chatbot arena&amp;#x27;s elo ranking https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboar...&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2312.07910/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
