We introduce RAGAs (Retrieval Augmented Generation Assessment), a framework for
reference-free evaluation of Retrieval Augmented Generation (RAG) pipelines. RAG …

We present a study of retrieval-augmented language models (LMs) on long-form question
answering. We analyze how retrieval augmentation impacts different LMs, by comparing …

Retrieval-augmented generation (RAG) can enhance the generation quality of large
language models (LLMs) by incorporating external token databases. However, retrievals …

Retrieval-augmented language models pose a promising alternative to standard language
modeling. During pretraining, these models search in a corpus of documents for contextually …

$ K $-nearest neighbor language models ($ k $ NN-LMs), which integrate retrieval with next-
word prediction, have demonstrated strong performance in language modeling as well as …

Abstract We introduce PersonaLM-Domain-distributed Span-Aggregated K-nearest N-gram
retrieval augmentation to improve language modeling for Automatic Speech Recognition …

In this paper, we introduce MoRSE (Mixture of RAGs Security Experts), the first specialised
AI chatbot for cybersecurity. MoRSE aims to provide comprehensive and complete …

We introduce Chunk-Distilled Language Modeling (CD-LM), an approach to text generation
that addresses two challenges in current large language models (LLMs): the inefficiency of …

Large language models (LLMs) often hallucinate and lack the ability to provide attribution for
their generations. Semi-parametric LMs, such as kNN-LM, approach these limitations by …

Text generation is an important emerging AI technology that has seen significant research
advances in recent years. Due to its closeness to how humans communicate, mastering text …