Answer Engines powered by Generative AI are reshaping how people access and interact with global knowledge and online information. This repository provides the code and data necessary to reproduce our experimental results in this area, advancing research on the evaluation of Answer Engines and their underlying RAG (Retrieval-Augmented Generation) systems.

Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses

TBA

Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question Coverage

@article{xie2024rag,
  title={Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question Coverage},
  author={Xie, Kaige and Laban, Philippe and Choubey, Prafulla Kumar and Xiong, Caiming and Wu, Chien-Sheng},
  journal={arXiv preprint arXiv:2410.15531},
  year={2024}
}