Retrieval Augmented Generation

TL;DR Retrieval-Augmented Generation (RAG) grounds large-language-model (LLM) answers in external documents, yet hallucinations persist. Existing detectors either rely on expensive LLM as a judges or on encoder classifiers that truncate context and lose evidence. LettuceDetect introduces a long-context, token-level classifier built on ModernBERT. It surpasses prior encoder baselines while remaining markedly more efficient than LLM-based judges. Background LLMs hallucinate when generated claims are not supported by retrieved context. Encoder detectors shorten inputs to fit model limits (context size), reducing recall, whereas generative judges process full context but incur high latency and cost....

TL;DR Generative Large Language Models (LLMs) are limited to generate text based on their training data which means any extension to additional sources necessitates additional training. Retrieval Augmented Generation (RAG) is a method that combines the use of a database with LLMs enables updating the LLM knowledge and make it more precise for specific applications. Method Building blocks The method consists of 3 building blocks. Document index. A pre-trained model was used to encode documents into embeddings to create the index....