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README.md

@@ -18,15 +18,14 @@ license: cc-by-nc-4.0
 This HF repository hosts instruction fine-tuned multilingual BLOOM model using the parallel instruction dataset called Bactrain-X in 52 languages. 
 We progressively add a language during instruction fine-tuning at each time, and train 52 models in total. Then, we evaluate those models in three multilingual benchmarks. 
 
-Please refer to our paper for more details.
+Please refer to [our paper](https://arxiv.org/abs/2404.04850) for more details.
 
-#### Instruction tuning details
 * Base model: [BLOOM 7B1](https://huggingface.co/bigscience/bloom-7b1)
 * Instruction languages: English, Chinese, Afrikaans, Arabic, Azerbaijani, Bengali, Czech, German, Spanish, Estonian, Farsi, Finnish, French, Galician, Gujarati, Hebrew, Hindi, Croatian, Indonesian, Italian, Japanese, Georgian, Kazakh, Khmer, Korean, Lithuanian, Latvian, Macedonian, Malayalam, Mongolian, Marathi, Burmese, Nepali, Dutch
 * Instruction language codes: en, zh, af, ar, az, bn, cs, de, es, et, fa, fi, fr, gl, gu, he, hi, hr, id, it, ja, ka, kk, km, ko, lt, lv, mk, ml, mn, mr, my, ne, nl
 * Training method: full-parameter fine-tuning.
 
-#### Usage
+### Usage
 The model checkpoint should be loaded using `transformers` library.
 
 ```python
@@ -36,9 +35,15 @@ tokenizer = AutoTokenizer.from_pretrained("MaLA-LM/lucky52-bloom-7b1-no-34")
 model = AutoModelForCausalLM.from_pretrained("MaLA-LM/lucky52-bloom-7b1-no-34")
 ```
 
-#### Citation
+### Citation
 ```
-@article{
+@misc{lucky52,
+  title         = "Lucky 52: How Many Languages Are Needed to Instruction Fine-Tune Large Language Models?",
+  author        = "Shaoxiong Ji and Pinzhen Chen",
+  year          = "2024",
+  eprint        = "2404.04850",
+  archiveprefix = "arXiv",
+  primaryclass  = "cs.CL"
 }
 ```