Update README

Author: jpablomch

EFTNAS/README.md

@@ -219,11 +219,9 @@ CUDA_VISIBLE_DEVICES=${DEVICES} torchrun --nproc_per_node=2 --master_port ${MAST
 
 # Citation
 
-If you use the code or data in your research, please use the following BibTex entry:
-
 ```
 @inproceedings{
-munoz2024eftnas,
+eftnas2024,
 title={Searching for Efficient Language Models in First-Order Weight-Reordered Super-Networks},
 author={J. Pablo Munoz and Yi Zheng and Nilesh Jain},
 booktitle={The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation},

LoNAS/README.md

@@ -119,4 +119,18 @@ CUDA_VISIBLE_DEVICES=${DEVICES} python run_math.py \
     --lora \
     --lora_weights trained_super_adapter/unified_math_10k/lonas-llama-7b \
     --nncf_config nncf_config/unified_math_10k/nncf_lonas_llama_7b.json
+```
+
+
+# Citation
+
+```
+@inproceedings{
+lonas2024,
+title={LoNAS: Elastic Low-Rank Adapters for Efficient Large Language Models},
+author={J. Pablo Munoz and Jinjie Yuan and Yi Zheng and Nilesh Jain},
+booktitle={The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation},
+year={2024},
+url={}
+}
 ```

README.md

@@ -10,6 +10,12 @@ EZNAS is a genetic programming-driven methodology for automatically discovering
 
 BootstrapNAS automates the generation of weight-sharing super-networks using the Neural Network Compression Framework (NNCF). 
 
+### [LoNAS: Elastic Low-Rank Adapters for Efficient Large Language Models](./LoNAS/README.md) 
+
+Initial exploration of using NAS on large language models by exploring a search space of elastic low-rank adapters
+while reducing memory and compute requirements of full-scale NAS, resulting in high-performing compressed models obtained from
+weight-sharing super-networks. We investigate the benefits and limitations of this method, motivating follow-up work.
+
 ### [EFTNAS: Searching for Efficient Language Models in First-Order Weight-Reordered Super-Networks](./EFTNAS/README.md)
 
 Integrating neural architecture search (NAS) and network pruning techniques, we effectively generate and train