mlsanityData to 7 Oct 2026

Retrieval Augmented Generation for Domain-specific Question Answering

arXiv 2404.14760 ↗First posted 23 Apr 2024cs.CL cs.AI cs.IRcited by 22

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

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.

Key facts. Cited by 22 papers in the map (data through 7 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.

Builds on

13 of 20 references.

References · 13

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  3. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
  4. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  5. 2019Language Models as Knowledge Bases?cited by 1,922
  6. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  7. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  8. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  9. 2023Benchmarking Large Language Models for News Summarizationcited by 444
  10. 2023ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledgecited by 324
  11. 2023Challenges and Applications of Large Language Modelscited by 251
  12. 2022Fine-tuning language models to find agreement among humans with diverse preferencescited by 189
1 more
  1. 2022Learning from Few Examples: A Summary of Approaches to Few-Shot Learningcited by 86

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 8; 2026: 10.

22 papers.

Sort:

By area:

  1. 2026Relevant Is Not Warranted: Evidence-Force Calibration for Cited RAGcited by 14
  2. 2025Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoningcited by 13
  3. 2025Federated Retrieval Augmented Generation for Multi-Product Question Answeringcited by 10
  4. 2024MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Modelscited by 9
  5. 2025Your RAG is Unfair: Exposing Fairness Vulnerabilities in Retrieval-Augmented Generation via Backdoor Attackscited by 8
  6. 2026PASC: Pipeline-Aware Conformal Prediction with Joint Coverage Guarantees for Multi-Stage NLP and LLM Pipelinescited by 7
  7. 2024Domain-specific Question Answering with Hybrid Searchcited by 5
  8. 2026UCCI: Calibrated Uncertainty for Cost-Optimal LLM Cascade Routingcited by 4
  9. 2024HALO: Hallucination Analysis and Learning Optimization to Empower LLMs with Retrieval-Augmented Context for Guided Clinical Decision Makingcited by 3
  10. 2025MHTS: Multi-Hop Tree Structure Framework for Generating Difficulty-Controllable QA Datasets for RAG Evaluationcited by 3
  11. 2025Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answeringcited by 3
  12. 2026How You Ask Matters! Adaptive RAG Robustness to Query Variationscited by 3
10 more
  1. 2024G-RAG: Knowledge Expansion in Material Sciencecited by 2
  2. 2025RAGAPHENE: A RAG Annotation Platform with Human Enhancements and Editscited by 1
  3. 2025Build AI Assistants using Large Language Models and Agents to Enhance the Engineering Education of Biomechanicscited by 1
  4. 2026Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QAcited by 1
  5. 2026Not All Queries Need Rewriting: When Prompt-Only LLM Refinement Helps and Hurts Dense Retrievalcited by 1
  6. 2025Answering Students' Questions on Course Forums Using Multiple Chain-of-Thought Reasoning and Finetuning RAG-Enabled LLMcited by 0
  7. 2026A Multi-Agent Human-LLM Collaborative Framework for Closed-Loop Scientific Literature Summarizationcited by 0
  8. 2026Guaranteeing Faithful Evidence Extraction in Speculative Retrieval-Augmented Generationcited by 0
  9. 2026CIG-MIA: Context-Induced Information Gain Membership Inference Attacks against Retrieval-Augmented Generationcited by 0
  10. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024KaPQA: Knowledge-Augmented Product Question-Answering
  2. 2024SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains
  3. 2024Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization
  4. 2024Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts
  5. 2024Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering
  6. 2023Prompt Generate Train (PGT): Few-shot Domain Adaption of Retrieval Augmented Generation Models for Open Book Question-Answering
  7. 2025Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation
  8. 2023PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter