# Closed-book QA

Google's T5 model achieves closed-book QA through pre-training, capable of answering questions without relying on external knowledge sources

T5-large-ssm-nqo is a closed-book QA model based on the T5 architecture, achieving knowledge retrieval and question answering through pretraining and fine-tuning.

A closed-book QA model based on the T5 architecture, pre-trained with denoising and salient span masking objectives, suitable for QA tasks without external knowledge sources.

T5-small-ssm-nq is a small closed-book QA model based on the T5 architecture, pre-trained and fine-tuned on C4, Wikipedia, and Natural Questions (NQ) datasets.

A trivia QA model based on T5-base architecture, achieving closed-book QA capability through context-free training

Google's T5 model, specifically designed for closed-book QA tasks, achieving high performance on natural questions through multi-stage training.

T5-3b-ssm-nq is a closed-book QA model based on the T5 architecture, achieving knowledge retrieval and QA capabilities through pretraining and fine-tuning.

T5-11b-ssm-nq is a large language model based on the T5 architecture, specifically designed for closed-book question answering tasks. Through pretraining and fine-tuning, this model can answer questions without external knowledge sources.

T5-xl-ssm-nq is a closed-book QA model based on the T5 architecture, excelling in natural questions through pretraining and fine-tuning.

A closed-book QA model based on T5 architecture, achieving retrieval-free question answering through pretraining and incremental training

T5-xxl SSM NQ is a closed-book QA model based on the T5 architecture, achieving knowledge storage and retrieval through pretraining and fine-tuning.

Google's T5 model for closed-book QA tasks, achieving high performance through pretraining and fine-tuning on the Trivia QA dataset.