Enhance performance of generative language models with self-consistency prompting on Amazon Bedrock

This article discusses how to enhance the performance of generative language models on complex tasks like arithmetic and multiple-choice reasoning using self-consistency prompting on Amazon Bedrock.

Specifically, the article covers:

• Overview of self-consistency prompting and how it differs from greedy chain-of-thought prompting
• Setting up batch inference with Amazon Bedrock Python SDK on a SageMaker notebook instance
• Formatting input data and running batch inference jobs for arithmetic reasoning on GSM8K dataset using Cohere Command model
• Results showing improved accuracy of Cohere Command on GSM8K using self-consistency over greedy decoding
• Example of using self-consistency prompting with AI21 Labs Jurassic-2 Mid model on AWS certification exam questions
• Considerations on efficiency and cost of using self-consistency prompting
• Conclusion and recommendations for using self-consistency prompting on Amazon Bedrock