model to learn from them and tailor its own generation accordingly. It’s like giving the model a reference point or target to aim for, improving the accuracy, style, and tone of its response to better match use the same piece of information in multiple prompts, you can store it in a variable and then reference that variable in each prompt. This makes a lot of sense when integrating prompts into your own top-K sampling methods. Available at: https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text#request_body. 5. Wei, J., et al., 2023, Zero Shot - Fine Tuned language models are zero shot

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Lai et al. (2017), DROP (Dua et al., 2019), C3 (Sun et al., 2019), and CMRC (Cui et al., 2019). Reference disambiguation datasets include WinoGrande Sakaguchi et al. (2019) and CLUEWSC (Xu et al., 2020)

into a shared representation and generate outputs in any of those formats. A single query can reference a paragraph and a diagram, and the model can respond with a spoken summary or an annotated image