Update README.md

README.md

@@ -28,7 +28,7 @@ We present TAT-LLM, a specialized language model crafted through the innovative
 
 ## Training
 
-We train our TAT-LLM model in various sizes, including 7B, 13B, and 70B, using different methods such as parameter-efficient fine-tuning and full-parameter fine-tuning of LLaMA 2 on a combination of financial data from the FinQA, TAT-QA, and TAT-DQA datasets([🤗HuggingFace Repo](https://huggingface.co/datasets/next-tat/tat-llm-instructions)). To refine accuracy, we introduce an External Executor, enhancing the model by processing intermediate outputs to derive conclusive answers. Please refer to the [paper](https://arxiv.org/abs/2401.13223) for more details.
+We train our TAT-LLM model in various sizes, including 7B, 13B, and 70B, using different methods such as parameter-efficient fine-tuning and full-parameter fine-tuning of LLaMA 2 on a combination of financial data from the FinQA, TAT-QA, and TAT-DQA training sets([🤗HuggingFace Repo](https://huggingface.co/datasets/next-tat/tat-llm-instructions)). To refine accuracy, we introduce an External Executor, enhancing the model by processing intermediate outputs to derive conclusive answers. Please refer to the [paper](https://arxiv.org/abs/2401.13223) for more details.
 
 ## Inference & Evaluation