Title: How RETRO Revolutionizes Language Modeling with Retrieval Enhanced Transformers
In the world of AI, language modeling has made impressive progress in recent years. One popular method is to increase the number of parameters in Transformer models, resulting in what are known as Large Language Models (LLMs) with 100+ billion parameters. However, this approach comes with a high training energy cost and requires massive datasets to facilitate the training of these models.
However, an alternative path for improving language models has emerged. This method, called RETRO (Retrieval Enhanced TRansfOrmers), augments transformers with retrieval over a database of text passages from various sources such as web pages, books, news, and code.
How RETRO works
With RETRO, the model does not rely solely on the data it was trained on. Instead, it has access to the entire training dataset through a retrieval mechanism. This allows for continuous improvements in language modeling performance as the size of the retrieval database increases, making it a more efficient and effective approach compared to the standard Transformer models.
The architecture of RETRO involves a combination of regular self-attention at a document level and cross-attention with retrieved neighbors at a finer passage level. This results in more accurate and factual continuations, as well as increased interpretability of model predictions.
Benefits of RETRO
In experiments conducted on a standard language modeling benchmark, a 7.5 billion parameter RETRO model outperformed larger models with 175 billion and 280 billion parameters on multiple datasets. Additionally, samples generated by the RETRO model were found to be more factual and on-topic compared to baseline models.
In conclusion, RETRO offers a promising strategy to enhance language modeling by utilizing retrieval mechanisms, leading to significant improvements in performance and interpretability. Its potential to revolutionize the field of language modeling makes it a noteworthy development in the world of AI.