13 мая 2024 г. · We examine and compare several quantifiable metrics of the Retrieval and Generation components, such as relevance, accuracy, and faithfulness, within the ...

The official repository for the paper: Evaluation of Retrieval-Augmented Generation: A Survey Arxiv. This paper has been accepted by the 2024 CCF Big Data.

3 июл. 2024 г. · Retrieval-Augmented Generation (RAG) has recently gained traction in natural language processing. Numerous studies and real-world applications.

11 апр. 2024 г. · Retrieval Augmented Generation (RAG) techniques address the issues of missing dynamic ,external information missing from LLMs, by y incorporating external ...

11 июл. 2024 г. · Evaluating retrieval-augmented generation (RAG) presents challenges, particularly for retrieval models within these systems.

20 июн. 2024 г. · In this post, we show you how to evaluate the performance, trustworthiness, and potential biases of your RAG pipelines and applications on Amazon Bedrock.

9 июн. 2024 г. · In this article, we will explore five essential metrics for RAG evaluation: Precision, Recall, Mean Reciprocal Rank (MRR), Mean Average Precision (MAP), and ...

1 нояб. 2023 г. · The purpose of evaluating your retriever is to determine the relevance of its document selection relative to a query. For example, if all the ...

Evaluating retrieval-augmented generation (RAG) systems traditionally relies on hand annotations for input queries, passages to retrieve, and responses to ...

3 июл. 2024 г. · This paper presents a comprehensive analysis of the challenges associated with evaluating Retrieval-Augmented Generation (RAG) systems.