Update README

README.md

@@ -19,6 +19,10 @@ python eval.py --model_path <path to model>
 
 Refer to our [code repository](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Mamba-Shedder) for the environment information to run this command.
 
+## Ethical Considerations
+
+Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.
+
 ## Model Sources
 
 - **Repository:** [https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Mamba-Shedder](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Mamba-Shedder)
@@ -27,10 +31,21 @@ Refer to our [code repository](https://github.com/IntelLabs/Hardware-Aware-Autom
 ## Citation
 
 ```bibtex
-@article{munoz2025mambashedder,
+@inproceedings{munoz2025mambashedder,
   title = {Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models},
-  author = {J. Pablo Munoz and Jinjie Yuan and Nilesh Jain},
-  journal = {},
-  year = {2025}
+  author = {Mu{\~n}oz, J. Pablo  and      Yuan, Jinjie  and      Jain, Nilesh},
+  booktitle = "Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL 2025)",
+    month = jun,
+    year = "2025",
+    address = "Albuquerque, New Mexico",
+    publisher = "Association for Computational Linguistics",
+    url = "",
 }
 ```
+
+### Original Work Citation  
+This work builds upon work done by the State-Spaces team.  Please see the following for additional citations of their work:  
+
+**Repository:** ([state-spaces/mamba](https://github.com/state-spaces/mamba)  
+**Paper:** [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://arxiv.org/abs/2312.00752)  
+