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Company profile: BeingDatum.com
Trainers are ex-UpGrad, ex-Great Learning with some 25+ yrs experienced Statisticians.
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The Art Of Decoding Data Science.
Please forward this link to those who are interested to join our next batch of Data Science and Engineering program: https://chat.whatsapp.com/HOGlAyerFjp5bcFncI1UiF Company profile: BeingDatum.com Trainers are ex-UpGrad, ex-Great Learning with some 25+…
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BEINGDATUM - FLAGSHIP COURSES - DATA SCIENCE
Job Oriented Integrated Certification
Website: BeingDatum.com
For any doubts, reach us at: +91-9916812177
Website: BeingDatum.com
For any doubts, reach us at: +91-9916812177
The Art Of Decoding Data Science.
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BEINGDATUM - FLAGSHIP COURSES - DATA SCIENCE
Job Oriented Integrated Certification
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For any doubts, reach us at: +91-9916812177
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For any doubts, reach us at: +91-9916812177
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
.
.
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
lnkd.in
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GitHub - microsoft/unilm: Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
https://github.com/microsoft/unilm
https://github.com/microsoft/unilm
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GitHub - microsoft/unilm: Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities - microsoft/unilm