Update README.md

README.md

@@ -15,6 +15,11 @@ This task tests whether ultra-large language models are able to read a short sto
 
 Note: This repo is managed by the original author of this task.
 
+Please cite the following work:
+
+- Allen Nie, Yuhui Zhang, Atharva Shailesh Amdekar, Chris Piech, Tatsunori B. Hashimoto, and Tobias Gerstenberg. "Moca: Measuring human-language model alignment on causal and moral judgment tasks." Advances in Neural Information Processing Systems 36 (2023): 78360-78393.
+- Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Lalit K. Jain et al. "Big-bench extra hard." arXiv preprint arXiv:2502.19187 (2025).
+
 🌐 Data reader: https://moca-llm.github.io/causal_stories/1/ 
 
 <img src="https://cdn-uploads.huggingface.co/production/uploads/649466d90560480110a72247/OpQmyqL2UJ7yCuN5PtdCh.png" width="50%">
@@ -28,11 +33,6 @@ Note: This repo is managed by the original author of this task.
 
 This HuggingFace dataset version is the same as the BBEH version, with additional annotations from the human annotators (such as the causal structure of each story).
 
-Please cite the following work:
-
-- Allen Nie, Yuhui Zhang, Atharva Shailesh Amdekar, Chris Piech, Tatsunori B. Hashimoto, and Tobias Gerstenberg. "Moca: Measuring human-language model alignment on causal and moral judgment tasks." Advances in Neural Information Processing Systems 36 (2023): 78360-78393.
-- Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Lalit K. Jain et al. "Big-bench extra hard." arXiv preprint arXiv:2502.19187 (2025).
-
 ## What is the task trying to measure?
 This task attempts to measure models' ability to synthesize multiple potential causes and effects to reach an actionable conclusion.