diff --git a/data/xml/2024.bea.xml b/data/xml/2024.bea.xml
index 1b0ce81bb3..40af6cf92e 100644
--- a/data/xml/2024.bea.xml
+++ b/data/xml/2024.bea.xml
@@ -366,8 +366,10 @@
       <author><first>Giora</first><last>Alexandron</last><affiliation>Weizmann Institute of Science</affiliation></author>
       <pages>391-402</pages>
       <abstract>Unsupervised clustering of student responses to open-ended questions into behavioral and cognitive profiles using pre-trained LLM embeddings is an emerging technique, but little is known about how well this captures pedagogically meaningful information. We investigate this in the context of student responses to open-ended questions in biology, which were previously analyzed and clustered by experts into theory-driven Knowledge Profiles (KPs).Comparing these KPs to ones discovered by purely data-driven clustering techniques, we report poor discoverability of most KPs, except for the ones including the correct answers. We trace this ‘discoverability bias’ to the representations of KPs in the pre-trained LLM embeddings space.</abstract>
-      <url hash="1d8a7729">2024.bea-1.32</url>
+      <url hash="4dcdf183">2024.bea-1.32</url>
       <bibkey>gurin-schleifer-etal-2024-anna</bibkey>
+      <revision id="1" href="2024.bea-1.32v1" hash="1d8a7729"/>
+      <revision id="2" href="2024.bea-1.32v2" hash="4dcdf183" date="2024-09-17">Corrected a typo.</revision>
     </paper>
     <paper id="33">
       <title>Assessing Student Explanations with Large Language Models Using Fine-Tuning and Few-Shot Learning</title>