Update README.md

README.md

@@ -38,9 +38,9 @@ model-index:
         name: Solution Exact Match
 ---
 
-# Phi-3 Mini 4K Verbalized Rebus Solver 🇮🇹
+# Phi-3 Mini 4K Verbalized Rebus Solver - PEFT Adapters 🇮🇹
 
-This model is a parameter-efficient fine-tuned version of Phi-3 Mini 4K trained for verbalized rebus solving in Italian, as part of the [release](https://huggingface.co/collections/gsarti/verbalized-rebus-clic-it-2024-66ab8f11cb04e68bdf4fb028) for our paper [Non Verbis, Sed Rebus: Large Language Models are Weak Solvers of Italian Rebuses](TBD). The task of verbalized rebus solving consists of converting an encrypted sequence of letters and crossword definitions into a solution phrase matching the word lengths specified in the solution key. An example is provided below.
+This model is a parameter-efficient fine-tuned version of Phi-3 Mini 4K trained for verbalized rebus solving in Italian, as part of the [release](https://huggingface.co/collections/gsarti/verbalized-rebus-clic-it-2024-66ab8f11cb04e68bdf4fb028) for our paper [Non Verbis, Sed Rebus: Large Language Models are Weak Solvers of Italian Rebuses](https://arxiv.org/abs/2408.00584). The task of verbalized rebus solving consists of converting an encrypted sequence of letters and crossword definitions into a solution phrase matching the word lengths specified in the solution key. An example is provided below.
 
 The model was trained in 4-bit precision for 5070 steps on the verbalized subset of the [EurekaRebus](https://huggingface.co/datasets/gsarti/eureka-rebus) using QLora via [Unsloth](https://github.com/unslothai/unsloth) and [TRL](https://github.com/huggingface/trl). This repository contains PEFT-compatible adapters saved throughout training. Use the `revision=<GIT_HASH>` parameter in `from_pretrained` to load mid-training adapter checkpoints.
 
@@ -127,7 +127,15 @@ For problems or updates on this model, please contact [gabriele.sarti996@gmail.c
 If you use this model in your work, please cite our paper as follows:
 
 ```bibtex
-TBD
+@article{sarti-etal-2024-rebus,
+    title = "Non Verbis, Sed Rebus: Large Language Models are Weak Solvers of Italian Rebuses",
+    author = "Sarti, Gabriele and Caselli, Tommaso and Nissim, Malvina and Bisazza, Arianna",
+    journal = "ArXiv",
+    month = jul,
+    year = "2024",
+    volume = {abs/2408.00584},
+    url = {https://arxiv.org/abs/2408.00584},
+}
 ```
 
 ## Acknowledgements