Post by account_disabled on Jan 28, 2024 5:01:58 GMT
RETRO is a language model that is similar to REALM, RETRO attends to the retrieved documents in a more hierarchical way, which allows it to better understand the context of the documents. This results in text that is more fluent and coherent than text generated by REALM. Following RETRO, The teams developed an approach called Retrofit Attribution using Research and Revision (RARR) to help validate and implement the output of an LLM and cite sources. Retrofit Attribution using Research and Revision (RARR) RARR is a different approach to language modeling.
RARR does not generate text from scratch. Instead, it DB to Data retrieves a set of candidate passages from a corpus and then reranks them to select the best passage for the given task. This approach allows RARR to generate more accurate and informative text than traditional language models, but it can be more computationally expensive. These three implementations for RAG all have different strengths and weaknesses.
While what’s in production is likely some combination of innovations represented in these papers and more, the idea remains that documents and knowledge graphs are searched and used with a language model to generate a response. Based on the publicly shared information, we know that SGE uses a combination of the PaLM 2 and MuM language models with aspects of Google Search as its retriever. The implication is that Google’s document index and Knowledge Vault can both be used to fine-tune the responses. Bing got there first, but with Google’s strength in Search, there is no organization as qualified to use this paradigm to surface and personalize information.
RARR does not generate text from scratch. Instead, it DB to Data retrieves a set of candidate passages from a corpus and then reranks them to select the best passage for the given task. This approach allows RARR to generate more accurate and informative text than traditional language models, but it can be more computationally expensive. These three implementations for RAG all have different strengths and weaknesses.
While what’s in production is likely some combination of innovations represented in these papers and more, the idea remains that documents and knowledge graphs are searched and used with a language model to generate a response. Based on the publicly shared information, we know that SGE uses a combination of the PaLM 2 and MuM language models with aspects of Google Search as its retriever. The implication is that Google’s document index and Knowledge Vault can both be used to fine-tune the responses. Bing got there first, but with Google’s strength in Search, there is no organization as qualified to use this paradigm to surface and personalize information.