From 7f1ace98177aab4ae0a3514a13c4835fcf12340c Mon Sep 17 00:00:00 2001
From: anthology-assist <anthologygit@gmail.com>
Date: Fri, 11 Oct 2024 20:32:10 -0500
Subject: [PATCH] Paper Metadata: {2024.acl-tutorials.6}, closes #3928.

---
 data/xml/2024.acl.xml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/data/xml/2024.acl.xml b/data/xml/2024.acl.xml
index d110fea1ff..14ab8507b6 100644
--- a/data/xml/2024.acl.xml
+++ b/data/xml/2024.acl.xml
@@ -14393,12 +14393,12 @@
       <doi>10.18653/v1/2024.acl-tutorials.5</doi>
     </paper>
     <paper id="6">
-      <title>Detecting Machine-Generated Text: Techniques and Challenges</title>
-      <author><first>Li</first><last>Gao</last></author>
-      <author><first>Wenhan</first><last>Xiong</last></author>
-      <author><first>Taewoo</first><last>Kim</last></author>
+      <title>Watermarking for Large Language Model</title>
+      <author><first>Xuandong</first><last>Zhao</last></author>
+      <author><first>Yu-Xiang</first><last>Wang</last></author>
+      <author><first>Lei</first><last>Li</last></author>
       <pages>10-11</pages>
-      <abstract>As AI-generated text increasingly resembles human-written content, the ability to detect machine-generated text becomes crucial in many applications. This tutorial aims to provide a comprehensive overview of text detection techniques, focusing on machine-generated text and deepfakes. We will discuss various methods for distinguishing between human-written and machine-generated text, including statistical methods, neural network-based techniques, and hybrid approaches. The tutorial will also cover the challenges in the detection process, such as dealing with evolving models and maintaining robustness against adversarial attacks. By the end of the session, attendees will have a solid understanding of current techniques and future directions in the field of text detection.</abstract>
+      <abstract>As AI-generated text increasingly resembles human-written content, the ability to detect machine-generated text becomes crucial in both the computational linguistics and machine learning communities. In this tutorial, we aim to provide an in-depth exploration of text watermarking, a subfield of linguistic steganography with the goal of embedding a hidden message (the watermark) within a text passage. We will introduce the fundamentals of text watermarking, discuss the main challenges in identifying AI-generated text, and delve into the current watermarking methods, assessing their strengths and weaknesses. Moreover, we will explore other possible applications of text watermarking and discuss future directions for this field. Each section will be supplemented with examples and key takeaways.</abstract>
       <url hash="e3a066a0">2024.acl-tutorials.6</url>
       <bibkey>gao-etal-2024-detecting</bibkey>
       <doi>10.18653/v1/2024.acl-tutorials.6</doi>