# DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue

arXiv: https://arxiv.org/abs/2502.13847 · HTML: https://mlsanity.com/paper/2502.13847/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Conversational and speech RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 19 Feb 2025.

## Placed in

- Retrieval-augmented generation / [Conversational and speech RAG](https://mlsanity.com/rag/conversational-and-speech-rag/)

## Builds on (most cited first, 20 of 20)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression](https://mlsanity.com/paper/2310.06839/) (2023)
- [Beyond Goldfish Memory: Long-Term Open-Domain Conversation](https://mlsanity.com/paper/2107.07567/) (2021)
- [Selective Annotation Makes Language Models Better Few-Shot Learners](https://mlsanity.com/paper/2209.01975/) (2022)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering](https://mlsanity.com/paper/2404.17723/) (2024)
- [Retrieval-Augmented Generation for Code Summarization via Hybrid GNN](https://mlsanity.com/paper/2006.05405/) (2020)
- [Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers](https://mlsanity.com/paper/2404.07220/) (2024)
- [Few-Shot Conversational Dense Retrieval](https://mlsanity.com/paper/2105.04166/) (2021)
- [KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases](https://mlsanity.com/paper/2308.11761/) (2023)
- [Memory-augmented Dialogue Management for Task-oriented Dialogue Systems](https://mlsanity.com/paper/1805.00150/) (2018)

## Built on by (most cited first, 10 of 10)

- [Memorization and Knowledge Injection in Gated LLMs](https://mlsanity.com/paper/2504.21239/) (2025)
- [BMAM: Brain-inspired Multi-Agent Memory Framework](https://mlsanity.com/paper/2601.20465/) (2026)
- [Large Language Models for Oral History Understanding with Text Classification and Sentiment Analysis](https://mlsanity.com/paper/2508.06729/) (2025)
- [Context-Agent: Dynamic Discourse Trees for Non-Linear Dialogue](https://mlsanity.com/paper/2604.05552/) (2026)
- [Implementing Long Text Style Transfer with LLMs through Dual-Layered Sentence and Paragraph Structure Extraction and Mapping](https://mlsanity.com/paper/2505.07888/) (2025)
- [From Deferral to Learning: Online In-Context Knowledge Distillation for LLM Cascades](https://mlsanity.com/paper/2509.22984/) (2025)
- [Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA](https://mlsanity.com/paper/2602.09552/) (2026)
- [UniDial-EvalKit: A Unified Toolkit for Evaluating Multi-Faceted Conversational Abilities](https://mlsanity.com/paper/2603.23160/) (2026)
- [Improving Multi-turn Dialogue Consistency with Self-Recall Thinking](https://mlsanity.com/paper/2605.15102/) (2026)
- [An Auditable Symbolic-RAG-Generative AI Architecture for Goal-Oriented Conversation Orchestration](https://mlsanity.com/paper/2609.07152/) (2026)

## Abstract (arXiv)

Retrieval-Augmented Generation (RAG) systems have shown substantial benefits in applications such as question answering and multi-turn dialogue citeplewis2020retrieval. However, traditional RAG methods, while leveraging static knowledge bases, often overlook the potential of dynamic historical information in ongoing conversations. To bridge this gap, we introduce DH-RAG, a Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue. DH-RAG is inspired by human cognitive processes that utilize both long-term memory and immediate historical context in conversational responses citepstafford1987conversational. DH-RAG is structured around two principal components: a History-Learning based Query Reconstruction Module, designed to generate effective queries by synthesizing current and prior interactions, and a Dynamic History Information Updating Module, which continually refreshes historical context throughout the dialogue. The center of DH-RAG is a Dynamic Historical Information database, which is further refined by three strategies within the Query Reconstruction Module: Historical Query Clustering, Hierarchical Matching, and Chain of Thought Tracking. Experimental evaluations show that DH-RAG significantly surpasses conventional models on several benchmarks, enhancing response relevance, coherence, and dialogue quality.