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README.md
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## 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**_.
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#### Prompt Format
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Extractive QA:
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1. XQuAD, TyDiQA, MLQA, SQuAD
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| MT-LLaMA-7b | 88.0 | 54.9 | 52.2 | 49.6 | 79.1 |
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## Acknowledgement
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* Our training codes are largely borrowed from [FastChat](https://github.com/lm-sys/FastChat)
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* 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
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## 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**_.
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#### Prompt Format
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Extractive QA:
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1. XQuAD, TyDiQA, MLQA, SQuAD
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| MT-LLaMA-7b | 88.0 | 54.9 | 52.2 | 49.6 | 79.1 |
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## Acknowledgement
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* Our training codes are largely borrowed from [FastChat](https://github.com/lm-sys/FastChat)
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* 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
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