CaMeL’s architecture tackles this by treating the core LLM components as potentially untrustworthy black boxes and building a secure execution environment around them. It refines the “Dual LLM ...

Retrieval-augmented generation (RAG) removes this limitation by allowing the LLM to go get additional data as needed when prompted. Many companies are rushing to make their internal data sources ...

Notably, CaMeL's dual-LLM architecture builds upon a theoretical "Dual LLM pattern" previously proposed by Willison in 2023, which the CaMeL paper acknowledges while also addressing limitations ...

Retrieval Augmented Generation (RAG) is an architecture that augments the capabilities of a Large Language Model (LLM) like ChatGPT by adding an information retrieval system that provides grounding ...

To speed up the prefill of the long LLM inputs, one can pre-compute the KV cache of a text and re-use the KV cache when the context is reused as the prefix of another LLM input. However, the reused ...