# Retrieval Augmented Generation for Domain-specific Question Answering

arXiv: https://arxiv.org/abs/2404.14760 · HTML: https://mlsanity.com/paper/2404.14760/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge](https://mlsanity.com/paper/2303.14070/) (2023)
- [Challenges and Applications of Large Language Models](https://mlsanity.com/paper/2307.10169/) (2023)
- [Fine-tuning language models to find agreement among humans with diverse preferences](https://mlsanity.com/paper/2211.15006/) (2022)
- [Learning from Few Examples: A Summary of Approaches to Few-Shot Learning](https://mlsanity.com/paper/2203.04291/) (2022)

## Built on by (most cited first, 22 of 22)

- [Relevant Is Not Warranted: Evidence-Force Calibration for Cited RAG](https://mlsanity.com/paper/2605.28044/) (2026)
- [Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning](https://mlsanity.com/paper/2502.07912/) (2025)
- [Federated Retrieval Augmented Generation for Multi-Product Question Answering](https://mlsanity.com/paper/2501.14998/) (2025)
- [MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2408.17072/) (2024)
- [Your RAG is Unfair: Exposing Fairness Vulnerabilities in Retrieval-Augmented Generation via Backdoor Attacks](https://mlsanity.com/paper/2509.22486/) (2025)
- [PASC: Pipeline-Aware Conformal Prediction with Joint Coverage Guarantees for Multi-Stage NLP and LLM Pipelines](https://mlsanity.com/paper/2605.18812/) (2026)
- [Domain-specific Question Answering with Hybrid Search](https://mlsanity.com/paper/2412.03736/) (2024)
- [UCCI: Calibrated Uncertainty for Cost-Optimal LLM Cascade Routing](https://mlsanity.com/paper/2605.18796/) (2026)
- [HALO: Hallucination Analysis and Learning Optimization to Empower LLMs with Retrieval-Augmented Context for Guided Clinical Decision Making](https://mlsanity.com/paper/2409.10011/) (2024)
- [MHTS: Multi-Hop Tree Structure Framework for Generating Difficulty-Controllable QA Datasets for RAG Evaluation](https://mlsanity.com/paper/2504.08756/) (2025)
- [Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering](https://mlsanity.com/paper/2508.15213/) (2025)
- [How You Ask Matters! Adaptive RAG Robustness to Query Variations](https://mlsanity.com/paper/2604.10745/) (2026)
- [G-RAG: Knowledge Expansion in Material Science](https://mlsanity.com/paper/2411.14592/) (2024)
- [RAGAPHENE: A RAG Annotation Platform with Human Enhancements and Edits](https://mlsanity.com/paper/2508.19272/) (2025)
- [Build AI Assistants using Large Language Models and Agents to Enhance the Engineering Education of Biomechanics](https://mlsanity.com/paper/2511.15752/) (2025)
- [Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA](https://mlsanity.com/paper/2602.22584/) (2026)
- [Not All Queries Need Rewriting: When Prompt-Only LLM Refinement Helps and Hurts Dense Retrieval](https://mlsanity.com/paper/2603.13301/) (2026)
- [Answering Students' Questions on Course Forums Using Multiple Chain-of-Thought Reasoning and Finetuning RAG-Enabled LLM](https://mlsanity.com/paper/2511.09831/) (2025)
- [A Multi-Agent Human-LLM Collaborative Framework for Closed-Loop Scientific Literature Summarization](https://mlsanity.com/paper/2604.01452/) (2026)
- [Guaranteeing Faithful Evidence Extraction in Speculative Retrieval-Augmented Generation](https://mlsanity.com/paper/2609.10046/) (2026)
- [CIG-MIA: Context-Induced Information Gain Membership Inference Attacks against Retrieval-Augmented Generation](https://mlsanity.com/paper/2609.14649/) (2026)
- [Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning](https://mlsanity.com/paper/2610.01936/) (2026)

## Abstract (arXiv)

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.