What techniques ensure factual consistency in Generative AI used for scientific summarization

Question

With the help of proper code can you tell me What techniques ensure factual consistency in Generative AI used for scientific summarization?

Answer 1

Techniques like knowledge distillation, fine-tuning domain-specific datasets, and incorporating fact-checking mechanisms can ensure factual consistency in generative AI for scientific summarization. 

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• Pre-trained Model: Using a large pre-trained model like T5 helps leverage knowledge from extensive training data.
• Fine-tuning: Fine-tuning on domain-specific scientific datasets ensures the model learns to generate factually accurate summaries.
• Beam Search: Beam search is used to generate more precise and coherent summaries, reducing the risk of hallucinations.

Hence, factually consistent generative AI for scientific summarization can be achieved by fine-tuning on domain-specific data and using fact-checking mechanisms.