Update README.md

README.md

@@ -5,6 +5,8 @@
 
 **Qwen2-7B-Instruct-Refine** and **Qwen2-1.5B-Instruct-Refine** are two powerful large language models that act as proficient prompt engineers. They can optimize and refine the prompts input by users, and the generated optimized instructions can significantly enhance the LLM's ability to produce better and more informative responses for users.
 
+We sampled the dataset from OpenHermes and the LCCD dataset, ensuring a balanced task distribution. For training set annotations, we used Qwen-max with incorporated our handwritten examples as in-context prompts.
+
 ## 🚀 Quick Start
 
 Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
@@ -45,6 +47,8 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 
 ## 🔍 Evaluation
 
+We used single-turn instructions from MT-Bench as input for Qwen2-1.5B-Instruct and Qwen2-7B-Instruct. GPT4-turbo is used to evaluate the changes in the level of detail and truthfulness of responses to our model's revised instructions.
+
 | Model                              | Detail  | Truthfulness |
 |------------------------------------|---------|--------------|
 | Qwen2-1.5B-Instruct                | 50.00%  | 50.00%       |
@@ -52,4 +56,20 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 | + Qwen2-7B-Instruct-Refine         | 76.56%  | 62.19%       |
 | Qwen2-7B-Instruct                  | 50.00%  | 50.00%       |
 | + Qwen2-1.5B-Instruct-Refine       | 70.94%  | 57.19%       |
-| + Qwen2-7B-Instruct-Refine         | 74.69%  | 58.44%       |
+| + Qwen2-7B-Instruct-Refine         | 74.69%  | 58.44%       |
+
+## 📜 Citation
+
+If you find our work helpful, please cite it!
+
+```
+@misc{TAPIR,
+      title={Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning}, 
+      author={Yuanhao Yue and Chengyu Wang and Jun Huang and Peng Wang},
+      year={2024},
+      eprint={2405.13448},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2405.13448}, 
+}
+```