Update README.md

README.md

@@ -10,7 +10,7 @@ iKG ([Imperial](https://www.imperial.ac.uk/research-and-innovation/) Knowledge G
 - **Developed by**: [Xiaohui Li](https://xiaohui-victor-li.github.io/)
 - **Model type**: Auto-regressive language model based on the transformer architecture.
 - **License**: Non-commercial
-- **Finetuned from model**: [Vicuna-7B](https://huggingface.co/lmsys/vicuna-7b-v1.3) (originally from LLaMA).
+- **Finetuned from model**: [Vicuna-7B](https://huggingface.co/lmsys/vicuna-7b-v1.3) (originally from [LLaMA](https://arxiv.org/abs/2302.13971)).
 
 ## Model Sources
 - **Repository**: [https://github.com/your-github-repo](https://github.com/your-github-repo)
@@ -18,7 +18,7 @@ iKG ([Imperial](https://www.imperial.ac.uk/research-and-innovation/) Knowledge G
 - **Paper**: [https://arxiv.org/abs/your-paper-id](https://arxiv.org/abs/your-paper-id)
 
 ## Uses
-The primary use of iKG LLM is for generating knowledge graphs based on instruction-following capability with specialized prompt. It's intended for researchers, data scientists, and developers interested in natural language processing, and knowledge graph construction.
+The primary use of iKG LLM is for generating knowledge graphs (KG) based on instruction-following capability with specialized prompts. It's intended for researchers, data scientists, and developers interested in natural language processing, and knowledge graph construction.
 
 ## How to Get Started with the Model
 - **Python Code**: [https://github.com/your-github-repo/tree/main#api](https://github.com/your-github-repo/tree/main#api)
@@ -27,7 +27,7 @@ The primary use of iKG LLM is for generating knowledge graphs based on instructi
 ## Training Details
 iKG is fine-tuned from Vicuna-7B using ~3K instruction-following demonstrations including KG construction input document and extracted KG triplets as response output. iKG is thus learnt to extract list of KG triplets from given text document via prompt engineering. For more in-depth training details, refer to the "Generative Knowledge Graph Construction with Fine-tuned LLM" section of [the accompanying paper](https://arxiv.org/abs/your-paper-id).
 
-- **Prompt Template**>: The entities and relationship can be customized for specific tasks. `<input_text>` is the document text.
+- **Prompt Template**>: The entities and relationship can be customized for specific tasks. `<input_text>` is the document text to replace.
 
   ```
   From the provided document labeled as INPUT_TEXT, your task is to extract structured information from it in the form of triplet for constructing a knowledge graph. Each tuple should be in the form of ('h', 'type',  'r', 'o', 'type'), where 'h' stands for the head entity, 'r' for the relationship, and 'o' for the tail entity. The 'type' denotes the category of the corresponding entity. Do NOT include redundant triplets, NOT include triplets with relationship that occurs in the past.