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@@ -221,7 +221,10 @@ <h2 id="scihal25">SciHal2025: Hallucination Detection for Scientific Content </h
 	<p>
 	This shared task invites participants to develop and evaluate systems that detect hallucinations in automatically generated scientific answers. The dataset comprises research-oriented questions sourced from subject matter experts, along with corresponding answers and references. These answers are produced by various well-performing retrieval-augmented generation (RAG) systems indexing approximately millions of published academic abstracts. Each answer is annotated to indicate whether it includes unsupported claims that are not grounded in the provided references. Two levels of labeling will be provided: a three-class scheme (entailment, neutral, contradiction) and a more detailed scheme encompassing 10+ fine-grained categories (to be specified later). Teams are challenged to classify claims into the appropriate categories, with evaluation metrics focusing on the precision and recall of detecting unsupported claims.
 	</p>
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+	<p>
+	More information is available on <a href=scihal.html>the shared task page</a>.
+	</p>
 		
 	<h3>Organizers</h3>