Update README.md

README.md

@@ -16,7 +16,9 @@ ICKG (Integrated Contextual Knowledge Graph Generator) v3.2 is a knowledge graph
 ## Model Sources
 - **Repository**: [https://github.com/xiaohui-victor-li/FinDKG](https://github.com/xiaohui-victor-li/FinDKG)
 - **Website**: [https://xiaohui-victor-li.github.io/FinDKG/](https://xiaohui-victor-li.github.io/FinDKG/)
-- **Paper**: [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4608445](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4608445)
+- **Paper**:
+  - [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4608445](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4608445)
+  - [https://arxiv.org/abs/2407.10909](https://arxiv.org/abs/2407.10909)
 
 ## Use Guidance
 
@@ -28,9 +30,11 @@ The primary use of ICKG LLM is for generating knowledge graphs (KG) based on ins
 
 - ⚠️ As a prerequisite, ensure the Hugging Face python library `transformers` and the auxiliary `peft` library (https://github.com/huggingface/peft) are pre-installed.
 
-- The LLM is best suited to run on a GPU server  
+- 📒 For optimal performance, the LLM is recommended to run on a GPU server.
+
 
 ## How to Get Started with the Model
+
 - **Python Code**: [https://github.com/xiaohui-victor-li/FinDKG](https://github.com/xiaohui-victor-li/FinDKG)
 
 ## Training Details