The National Institute of Informatics (NII, Director-General: Sadao Kurohashi, located in Chiyoda-ku, Tokyo) has been hosting the Large Language Model Study Group (LLM-jp) since May of this year.

Only the strong will survive, but analyst says cull will not be as rapid as during dotcom era Gartner says the market for large language model (LLM) providers is on the cusp of an extinction phase ...

Getting under the hood. RAG is a process that improves the accuracy, currency and context of LLMs like GPT4. They work by combining a pre-trained LLM with a retrieval component that is connected ...

A subsequent phase, dubbed “Pre-RL-Zero,” introduces more active LLM participation during inference. These techniques involved multi-turn interactions, interleaving query generation, retrieval ...

Using an LLM (GPT-4, for instance) with RAG and GraphRAG approaches and adding access to external APIs, we have built a proof-of-concept that shows what can be the future of automation in SEO.

While RAG is a powerful AI technique for improving any LLM’s generative accuracy, it’s not without some limitations. The quality of RAG’s responses is only as good as its underlying ...

And in general, RAG adds complexity to the LLM application, requiring the development, integration and maintenance of additional components. The added overhead slows the development process. Cache ...

Its specialized RAG agents easily surpassed the performance of well-known frontier models like OpenAI’s GPT-4o and Anthropic PBC’s Claude 3.5 Sonnet in areas such as document understanding ...

As Maxime Vermeir, senior director of AI strategy at ABBYY, a leading company in document processing and AI solutions, explained: "RAG enables you to combine your vector store with the LLM itself.