Retrieval Augmented Generation for Domain-specific Question Answering

Sanat Sharma, David Seunghyun Yoon, Franck Dernoncourt, Dewang Sultania, Karishma Bagga, Mengjiao Zhang, Trung Bui, Varun Kotte

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.

• Adobe
• StepFun
• Chinese Academy of Sciences
• Peking University
• The Chinese University of Hong Kong
• Zhongguancun Academy
• Adobe
• Lancaster University
• University of Bristol
• Adobe
• University of Toronto
• Adobe
• Michigan State University
• Adobe
• Fudan University
• Shanghai Innovation Institute
• Westlake University
• Zhejiang University
• Adobe
• State University of New York at Buffalo
• The Ohio State University
• University of Maryland College Park