Update README.md

README.md

@@ -10,7 +10,7 @@ license: llama3.1
 # Llama 3.1 RDS+ Tulu 3 Multitask 326k
 
 This is a model trained on 939k samples selected by RDS+ from the [Tulu 3 unfiltered dataset](https://huggingface.co/datasets/hamishivi/tulu-3-unfiltered).
-For more details, please see the paper [Practical Large-Scale Data Selection for Instruction Tuning](todo) and [associated codebase](https://github.com/hamishivi/automated-instruction-selection).
+For more details, please see the paper [Practical Large-Scale Data Selection for Instruction Tuning](https://arxiv.org/abs/2503.01807) and [associated codebase](https://github.com/hamishivi/automated-instruction-selection).
 
 <center>
 <img src="https://huggingface.co/hamishivi/tulu-2-multitask-rrmax-326k-sft/resolve/main/image.png" alt="Practical Large-Scale Data Selection for Instruction Tuning logo" width="200px"/>
@@ -31,7 +31,7 @@ For more details, please see the paper [Practical Large-Scale Data Selection for
 
 ## Results
 
-For more results and analysis, please see [our paper](todo).
+For more results and analysis, please see [our paper](https://arxiv.org/abs/2503.01807).
 
 | Method                | MMLU  | GSM8k | BBH  | TydiQA | Codex | Squad | AlpacaEval | Average |
 |-----------------------|------:|------:|-----:|-------:|------:|------:|-----------:|--------:|
@@ -77,7 +77,8 @@ If you find this model or data is useful in your work, please cite it with:
       title={{Practical Large-Scale Data Selection for Instruction Tuning}}, 
       author={{Hamish Ivison and Muru Zhang and Faeze Brahman and Pang Wei Koh and Pradeep Dasigi}}
       year={2025},
-      eprint={todo},
+      url={https://arxiv.org/abs/2503.01807},
+      eprint={2503.01807},
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }