Update README.md

README.md

@@ -24,7 +24,7 @@ The primary use of ICKG LLM is for generating knowledge graphs (KG) based on ins
 - **Python Code**: [https://github.com/xiaohui-victor-li/FinDKG](https://github.com/xiaohui-victor-li/FinDKG)
 
 ## Training Details
-ICKG 2.0 is fine-tuned from the latest 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://papers.ssrn.com/sol3/papers.cfm?abstract_id=4608445).
+ICKG 2.0 is fine-tuned from the latest Vicuna-7B using ~3K instruction-following demonstrations including KG construction input document and extracted KG triplets as response output. ICKG 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://papers.ssrn.com/sol3/papers.cfm?abstract_id=4608445).
 
 - **Prompt Template**: The entities and relationship can be customized for specific tasks. `<input_text>` is the document text to replace.
 
@@ -64,7 +64,7 @@ ICKG 2.0 is fine-tuned from the latest Vicuna-7B using ~3K instruction-following
   ```
 
 ## Evaluation
-ICKG has undergone preliminary evaluation comparing its performance to GPT-3.5, GPT-4, and the original Vicuna-7B model. With respect to the KG construction task, it outperforms GPT-3.5 and Vicuna-7B while exhibiting comparative capability as GPT-4. iKG excels in generating instruction-based knowledge graphs with a particular emphasis on quality and adherence to format.
+ICKG has undergone preliminary evaluation comparing its performance to GPT-3.5, GPT-4, and the original Vicuna-7B model. With respect to the KG construction task, it outperforms GPT-3.5 and Vicuna-7B while exhibiting comparative capability as GPT-4. ICKG excels in generating instruction-based knowledge graphs with a particular emphasis on quality and adherence to format.
 
 For a more detailed introduction, refer to [the accompanying paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4608445).