Retrieval Augmented Generation for Domain-specific Question Answering
<|reference_start|>Retrieval Augmented Generation for Domain-specific Question Answering: Question answering (QA) has become an important application in the advanced development of large language models. General pre-trained large language models for question-answering are not trained to properly understand the knowledge or terminology for a specific domain, such as finance, healthcare, education, and customer service for a product. To better cater to domain-specific understanding, we build an in-house question-answering system for Adobe products. We propose a novel framework to compile a large question-answer database and develop the approach for retrieval-aware finetuning of a Large Language model. We showcase that fine-tuning the retriever leads to major improvements in the final generation. Our overall approach reduces hallucinations during generation while keeping in context the latest retrieval information for contextual grounding.<|reference_end|>
@article{sharma2024retrieval,
    title={Retrieval Augmented Generation for Domain-specific Question Answering},
    author={Sanat Sharma, David Seunghyun Yoon, Franck Dernoncourt, Dewang
  Sultania, Karishma Bagga, Mengjiao Zhang, Trung Bui, Varun Kotte},
    journal={arXiv preprint arXiv:2404.14760},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14760},
    primaryClass={cs.CL cs.AI cs.IR cs.LG}
}