@inproceedings{krayko-etal-2024-efficient,
    title = "Efficient Answer Retrieval System ({EARS}): Combining Local {DB} Search and Web Search for Generative {QA}",
    author = "Krayko, Nikita  and
      Sidorov, Ivan  and
      Laputin, Fedor  and
      Galimzianova, Daria  and
      Konovalov, Vasily",
    editor = "Dernoncourt, Franck  and
      Preo{\c{t}}iuc-Pietro, Daniel  and
      Shimorina, Anastasia",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2024",
    address = "Miami, Florida, US",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.emnlp-industry.116/",
    doi = "10.18653/v1/2024.emnlp-industry.116",
    pages = "1584--1594",
    abstract = "In this work, we propose an efficient answer retrieval system **EARS**: a production-ready, factual question answering (QA) system that combines local knowledge base search with generative, context-based QA. To assess the quality of the generated content, we devise comprehensive metrics for both manual and automatic evaluation of the answers to questions. A distinctive feature of our system is the Ranker component, which ranks answer candidates based on their relevance. This feature enhances the effectiveness of local knowledge base retrieval by 23{\%}. Another crucial aspect of our system is the LLM, which utilizes contextual information from a web search API to generate responses. This results in substantial 92.8{\%} boost in the usefulness of voice-based responses. **EARS** is language-agnostic and can be applied to any data domain."
}