Summary of Retrieval Augmented Generation For Domain-specific Question Answering, by Sanat Sharma et al.
Retrieval Augmented Generation for Domain-specific Question Answering
by Sanat Sharma, David Seunghyun Yoon, Franck Dernoncourt, Dewang Sultania, Karishma Bagga, Mengjiao Zhang, Trung Bui, Varun Kotte
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework for building a question-answering system tailored to specific domains, such as finance or healthcare. By fine-tuning a Large Language Model (LLM) with a retriever-aware approach, the authors achieve significant improvements in QA performance while reducing hallucinations and maintaining contextual grounding. The system is specifically designed for Adobe products, leveraging a large question-answer database for retrieval-aware finetuning of the LLM. This work demonstrates the importance of domain-specific understanding in large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand questions about specific topics like finance or healthcare. Right now, big language models are not good at this because they were trained on too much general information and not enough specific knowledge. The authors created a special question-answering system for Adobe products that can answer questions more accurately by fine-tuning a large language model to focus on relevant details. |
Keywords
* Artificial intelligence * Fine tuning * Grounding * Large language model * Question answering
