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Amazon outperforms GPT-3.5 by 16%!
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Amazon's model, trained with 1 billion parameters, has surpassed the previous state-of-the-art LLM (#GPT-3.5) by 16%, achieving a remarkable accuracy rate of 91.68% compared to the GPT's rate of 75.17%. This impressive feat was achieved by generating intermediate reasoning steps for prompting demonstrations, also known as chain-of-thought (#CoT) prompting.

Amazon's approach integrates both language (text) and vision (images) modalities through a two-stage architecture that distinguishes between generating rationales and inferring answers. This way, the answer inference process can benefit from more robust rationales generated from multimodal information (GPT-3.5).

To put it simply, the model is enhanced with an additional layer that enables it to produce more insightful rationales and answers based on a broader range of knowledge.

The study, code, and datasets are publicly available:

1. Study: https://lnkd.in/g2Xn4462     
2. Code: https://lnkd.in/gJ7BxVJJ
3. Datasets: https://lnkd.in/gdp6NF9k

GitHub - microsoft/unilm: Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
https://github.com/microsoft/unilm

2201.00650.pdf