Researchers from Nvidia and the University of Illinois at Urbana-Champaign have developed Retro 48B, a larger language model that improves on previous retrieval-augmented models. By pre-training with retrieval on a vast corpus, Retro 48B enhances task performance in question answering. The study demonstrates the potential of larger retrieval-augmented models in natural language understanding.
Researchers from NVIDIA Introduce Retro 48B: The Largest LLM Pretrained with Retrieval before Instruction Tuning
Researchers from Nvidia and the University of Illinois at Urbana Champaign have developed Retro 48B, a language model that surpasses previous models in size and performance. Retro 48B is pre-trained using retrieval on a large dataset, resulting in improved perplexity and factuality. This model demonstrates the potential of larger retrieval-augmented models in natural language understanding.
Key Findings:
– Retro 48B improves zero-shot question answering compared to traditional language models.
– InstructRetro, the decoder of Retro 48B, achieves similar results even without the encoder, highlighting the effectiveness of retrieval-based pre-training.
– InstructRetro significantly enhances zero-shot accuracy in a wide range of open-ended question-answering tasks.
– Pre-training with retrieval using the Retro augmentation method improves perplexity and factual accuracy.
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