<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Nlp on Yingfa Chen 陈英发</title><link>https://chen-yingfa.github.io/tags/nlp/</link><description>Recent content in Nlp on Yingfa Chen 陈英发</description><generator>Hugo -- 0.146.6</generator><language>en-us</language><lastBuildDate>Wed, 10 Jan 2024 10:38:38 +0000</lastBuildDate><atom:link href="https://chen-yingfa.github.io/tags/nlp/index.xml" rel="self" type="application/rss+xml"/><item><title>InfiniteBench: Extending Long Context Evaluation Beyond 100K Tokens</title><link>https://chen-yingfa.github.io/research_posts/2024-infinitebench/</link><pubDate>Wed, 10 Jan 2024 10:38:38 +0000</pubDate><guid>https://chen-yingfa.github.io/research_posts/2024-infinitebench/</guid><description>&lt;p>&lt;a href="http://www.github.com/OpenBMB/InfiniteBench">Code&lt;/a> | &lt;a href="https://arxiv.org/abs/2402.13718">Paper&lt;/a>&lt;/p>
&lt;p>The first benchmark for evaluating the effectiveness of LLMs in handling more than 100k tokens!&lt;/p>
&lt;blockquote>
&lt;p>In the paper, we name it $\infty$-Bench, but I will sometimes use &amp;ldquo;InfiniteBench&amp;rdquo; in this blog post for better readability.&lt;/p>&lt;/blockquote>
&lt;p>Finally got some time to write this blog, been so busy lately! I have been in a fairly long duration of research hiatus, meanwhile the field of NLP has been revolutionized by an overwhelming number of new LLMs. Finally, I was able to arrive at some productive and meaningful work in this new era of research, as a second author. In this blog post, I will introduce this work that I have been working on recently.&lt;/p></description></item></channel></rss>