Add citation info and editing modes.

README.md

@@ -40,15 +40,21 @@ The `medit-xxl` model was obtained by fine-tuning the `MBZUAI/bactrian-x-llama-1
 ### Model Sources
 
 - **Repository:** https://github.com/vipulraheja/medit
-- **Paper:** TBA
+- **Paper:** https://arxiv.org/abs/2402.16472v1
 
 ## How to use
 
+Given an edit instruction and an original text, our model can generate the edited version of the text.<br>
+
+![task_specs](https://cdn-uploads.huggingface.co/production/uploads/60985a0547dc3dbf8a976607/816ZY2t0XPCpMMd6Z072K.png)
+
+Specifically, our models support both multi-lingual and cross-lingual text revision. Note that the input and output texts are always in the same language. The monolingual
+vs. cross-lingual setting is determined by comparing the language of the edit instruction in relation to the language of the input text.
+
 ### Instruction format
 
 Adherence to the following instruction format is essential; failure to do so may result in the model producing less-than-ideal results.
 
-
 ```
 instruction_tokens = [
     "Instruction",
@@ -73,14 +79,16 @@ task_descriptions = [
     "Umschreiben Sie den Satz",                 # <-- Paraphrasing
     ...
 ]
+```
 
-The entire list of possible instruction, input, output tokens, and task descriptions can be found in the Appendix of our paper.
+**The entire list of possible instructions, input/output tokens, and task descriptions can be found in the Appendix of our paper.**
 
+```
+prompt_template = """### <instruction_token>:\n<task_description>\n### <input_token>:\n<input>\n### <output_token>:\n\n"""
+```
 
-prompt_template = """### <instruction_token>:\n<task description>\n### <input_token>:\n<input>\n### <output_token>:\n\n"""
+Note that the tokens and the task description need not be in the language of the input (in the case of cross-lingual revision).
 
-Note that the tokens and the task description need not be in the language of the input.
-```
 
 ### Run the model
 
@@ -92,7 +100,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 
 model = AutoModelForCausalLM.from_pretrained(model_id)
 
-# English GEC
+# English GEC using Japanese instructions
 prompt = '### 命令:\n文章を文法的にする\n### 入力:\nI has small cat ,\n### 出力:\n\n'
 
 inputs = tokenizer(prompt, return_tensors='pt')
@@ -103,10 +111,29 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True)
 
 # --> I have a small cat ,
 
-# German GEC
-
+# German GEC using Japanese instructions
 prompt = '### 命令:\n文章を文法的にする\n### 入力:\nIch haben eines kleines Katze ,\n### 出力:\n\n'
 
 # ...
 # --> Ich habe eine kleine Katze ,
 ```
+
+#### Software
+https://github.com/vipulraheja/medit
+
+## Citation
+
+**BibTeX:**
+```
+@article{raheja2023medit,
+      title={mEdIT: mEdIT: Multilingual Text Editing via Instruction Tuning}, 
+      author={Vipul Raheja and Dimitris Alikaniotis and Vivek Kulkarni and Bashar Alhafni and Dhruv Kumar},
+      year={2024},
+      eprint={2402.16472v1},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+**APA:**
+Raheja, V., Alikaniotis, D., Kulkarni, V., Alhafni, B., & Kumar, D. (2024). MEdIT: Multilingual Text Editing via Instruction Tuning. ArXiv. /abs/2402.16472