Update README.md

README.md

@@ -26,9 +26,11 @@ You can try the codes from [this repo](https://github.com/DAMO-NLP-SG/MT-LLaMA).
 
                                           
 ## Zero-shot Evaluation
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 We primarily follow the protocols of [Bigscience T0](https://openreview.net/forum?id=9Vrb9D0WI4) to assess the generalization capability of our Multi-task LLaMA to: (1) _**Unseen Datasets**_ (i.e., datasets from seen tasks); (2) _**Unseen Tasks**_.
                                                      
-#### Prompt Format                                                     
+#### Prompt Format
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 Extractive QA:
 
 1. XQuAD, TyDiQA, MLQA, SQuAD
@@ -95,6 +97,7 @@ Natural Language Inference:
 | MT-LLaMA-7b | 88.0        | 54.9               | 52.2       | 49.6        | 79.1       |
 
 ## Acknowledgement
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 * Our training codes are largely borrowed from [FastChat](https://github.com/lm-sys/FastChat)
 * We are also grateful for the efforts of [LLaMA](https://github.com/facebookresearch/llama) (from FAIR) and [T0](https://github.com/bigscience-workshop/t-zero) (from BigScience), which serve as the foundation of our work