Update README.md

README.md

@@ -136,8 +136,11 @@ Results of the heuristic sub-network discoverd from the super-network:
 
 ## Model Sources
 
-- **Repository:** [https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears)
-- **Paper:** [Shears: Unstructured Sparsity with Neural Low-rank Adapter Search](https://arxiv.org/abs/2404.10934)
+**Repository:** [https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears)
+
+**Paper:** 
+- [Shears: Unstructured Sparsity with Neural Low-rank Adapter Search](https://arxiv.org/abs/2404.10934)
+- [Low-Rank Adapters Meet Neural Architecture Search for LLM Compression](https://arxiv.org/abs/2501.16372)
 
 ## Ethical Considerations
 
@@ -154,11 +157,23 @@ Intel is committed to respecting human rights and avoiding causing or contributi
 ## Citation
 
 ```bash
-@inproceedings{munoz2024shears,
-  title = {Shears: Unstructured Sparsity with Neural Low-rank Adapter Search},
-  author={J. Pablo Munoz and Jinjie Yuan and Nilesh Jain},
-  booktitle={The 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2024)},
-  year={2024}
+@inproceedings{munoz-etal-2024-shears,
+    title = "Shears: Unstructured Sparsity with Neural Low-rank Adapter Search",
+    author = "Mu{\~n}oz, J. Pablo  and
+      Yuan, Jinjie  and
+      Jain, Nilesh",
+    editor = "Yang, Yi  and
+      Davani, Aida  and
+      Sil, Avi  and
+      Kumar, Anoop",
+    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)",
+    month = jun,
+    year = "2024",
+    address = "Mexico City, Mexico",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.naacl-industry.34",
+    doi = "10.18653/v1/2024.naacl-industry.34",
+    pages = "395--405",
 }
 ```