mlsanityData to 7 Oct 2026

Adaptive Retrieval-Augmented Generation for Conversational Systems

arXiv 2407.21712 ↗First posted 31 Jul 2024cs.CL cs.IRcited by 22

On the map: Retrieval-augmented generation → Conversational and speech RAG

Abstract (arXiv)

Despite the success of integrating large language models into the development of conversational systems, many studies have shown the effectiveness of retrieving and augmenting external knowledge for informative responses. Hence, many existing studies commonly assume the always need for Retrieval Augmented Generation (RAG) in a conversational system without explicit control. This raises a research question about such a necessity. In this study, we propose to investigate the need for each turn of system response to be augmented with external knowledge. In particular, by leveraging human judgements on the binary choice of adaptive augmentation, we develop RAGate, a gating model, which models conversation context and relevant inputs to predict if a conversational system requires RAG for improved responses. We conduct extensive experiments on devising and applying RAGate to conversational models and well-rounded analyses of different conversational scenarios. Our experimental results and analysis indicate the effective application of RAGate in RAG-based conversational systems in identifying system responses for appropriate RAG with high-quality responses and a high generation confidence. This study also identifies the correlation between the generation's confidence level and the relevance of the augmented knowledge.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Placed in Conversational and speech RAG, one of 15 directions in Retrieval-augmented generation. Builds on 31 of its 44 references that are in the map. First posted on arXiv 31 Jul 2024.

Builds on

31 of 44 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems24 Jan

Other references · 30

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  6. 2016Layer Normalizationcited by 5,579
  7. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  8. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  9. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  10. 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
  11. 2023A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generationcited by 202
  12. 2022Ask Me Anything: A simple strategy for prompting language modelscited by 155
18 more
  1. 2024Evaluation of Retrieval-Augmented Generation: A Surveycited by 146
  2. 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
  3. 2020Knowledge-Grounded Dialogue Generation with Pre-trained Language Modelscited by 107
  4. 2018A Dataset for Document Grounded Conversationscited by 104
  5. 2024Evaluating Retrieval Quality in Retrieval-Augmented Generationcited by 90
  6. 2019Learning to Select Knowledge for Response Generation in Dialog Systemscited by 80
  7. 2022In conversation with Artificial Intelligence: aligning language models with human valuescited by 61
  8. 2020Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Accesscited by 58
  9. 2022Entity Cloze By Date: What LMs Know About Unseen Entitiescited by 55
  10. 2023Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generationcited by 54
  11. 2018Towards Universal Dialogue State Trackingcited by 33
  12. 2021Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representationcited by 32
  13. 2020GraphDialog: Integrating Graph Knowledge into End-to-End Task-Oriented Dialogue Systemscited by 29
  14. 2024Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Checkcited by 24
  15. 2024Language Models in Dialogue: Conversational Maxims for Human-AI Interactionscited by 21
  16. 2021Towards Zero and Few-shot Knowledge-seeking Turn Detection in Task-orientated Dialogue Systemscited by 3
  17. 2023Task2KB: A Public Task-Oriented Knowledge Basecited by 3
  18. 2023Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue Systemcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 11; 2026: 10.

22 papers.

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  1. 2025Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challengescited by 19
  2. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  3. 2026A Survey on LLM-based Conversational User Simulationcited by 14
  4. 2025Attributing Response to Context: A Jensen-Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generationcited by 12
  5. 2025A Survey of Scaling in Large Language Model Reasoningcited by 10
  6. 2025MedKGI: Iterative Differential Diagnosis with Medical Knowledge Graphs and Information-Guided Inquiringcited by 7
  7. 2025Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User Controlcited by 6
  8. 2026A2RAG: Adaptive Agentic Graph Retrieval for Cost-Aware and Reliable Reasoningcited by 6
  9. 2024TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAGcited by 2
  10. 2025Information Retrieval in the Age of Generative AI: The RGB Modelcited by 2
  11. 2025Fast, Slow, and Tool-augmented Thinking for LLMs: A Reviewcited by 2
  12. 2026Predicting Retrieval Utility and Answer Quality in Retrieval-Augmented Generationcited by 2
10 more
  1. 2026VikingMem: A Memory Base Management System for Stateful LLM-based Applicationscited by 2
  2. 2025Can Compact Language Models Search Like Agents? Distillation-Guided Policy Optimization for Preserving Agentic RAG Capabilitiescited by 1
  3. 2025A Survey of the State-of-the-Art in Conversational Question Answering Systemscited by 1
  4. 2026Predicting the Benefit of Retrieval Augmentation in Open-Domain Question Answeringcited by 1
  5. 2025Finding Diamonds in Conversation Haystacks: A Benchmark for Conversational Data Retrievalcited by 0
  6. 2026Training the Knowledge Base through Evidence Distillation and Write-Back Enrichmentcited by 0
  7. 2026Resonant Context Anchoring: Decoupling Attention Routing and Signal Gain at Inference Timecited by 0
  8. 2026Overcoming the Impedance Mismatch: A Theoretical Roadmap for Fusing Foundation Models and Knowledge Graphscited by 0
  9. 2026CoBRA: Learning Tool-Use Boundaries via Counterfactual Marginscited 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. 2024Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA
  2. 2026Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA
  3. 2024RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
  4. 2025Conversational Gold: Evaluating Personalized Conversational Search System using Gold Nuggets
  5. 2025MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systems
  6. 2026RAG-DIVE: A Dynamic Approach for Multi-Turn Dialogue Evaluation in Retrieval-Augmented Generation
  7. 2025RAGAPHENE: A RAG Annotation Platform with Human Enhancements and Edits
  8. 2024Toward Conversational Agents with Context and Time Sensitive Long-term Memory