mlsanityData to 7 Oct 2026

An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platforms

arXiv 2410.17694 ↗First posted 23 Oct 2024cs.CL cs.AIcited by 3

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

Abstract (arXiv)

Question Answering (QA) systems face challenges in handling complex questions that require multi-domain knowledge synthesis. The naive RAG models, although effective in information retrieval, struggle with complex questions that require comprehensive and in-depth answers. The pioneering task is defined as explanatory answer generation, which entails handling identified challenges such as the requirement for comprehensive information and logical coherence within the generated context. To address these issues, we refer to systematic thinking theory and propose SynthRAG, an innovative framework designed to enhance QA performance. SynthRAG improves on conventional models by employing adaptive outlines for dynamic content structuring, generating systematic information to ensure detailed coverage, and producing customized answers tailored to specific user inquiries. This structured approach guarantees logical coherence and thorough integration of information, yielding responses that are both insightful and methodically organized. Empirical evaluations underscore SynthRAG's effectiveness, demonstrating its superiority in handling complex questions, overcoming the limitations of naive RAG models, and significantly improving answer quality and depth. Furthermore, an online deployment on the Zhihu platform revealed that SynthRAG's answers achieved notable user engagement, with each response averaging 5.73 upvotes and surpassing the performance of 79.8% of human contributors, highlighting the practical relevance and impact of the proposed framework. Our code is available at https://github.com/czy1999/SynthRAG .

Key facts. Cited by 3 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 17 of its 30 references that are in the map. First posted on arXiv 23 Oct 2024.

Builds on

17 of 30 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering19 Jun
  2. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct

Other references · 15

  1. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  2. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,442
  3. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  4. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  5. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 705
  6. 2023Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedbackcited by 351
  7. 2022Re3: Generating Longer Stories With Recursive Reprompting and Revisioncited by 193
  8. 2024Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Expertscited by 107
  9. 2023WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferencescited by 100
  10. 2021Question Answering Over Temporal Knowledge Graphscited by 99
  11. 2023WebCPM: Interactive Web Search for Chinese Long-form Question Answeringcited by 93
  12. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
3 more
  1. 2023A Survey of Large Language Models Attributioncited by 71
  2. 2024Improving Retrieval Augmented Language Model with Self-Reasoningcited by 28
  3. 2019Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphscited by 21

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Modelscited by 89
  2. 2025Dynamic Orchestration of Multi-Agent System for Real-World Multi-Image Agricultural VQAcited by 1
  3. 2025A Question Answering Dataset for Temporal-Sensitive Retrieval-Augmented Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Augmenting Question Answering with A Hybrid RAG Approach
  2. 2025Typed-RAG: Type-Aware Decomposition of Non-Factoid Questions for Retrieval-Augmented Generation
  3. 2025RAGentA: Multi-Agent Retrieval-Augmented Generation for Attributed Question Answering
  4. 2024Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models
  5. 2024RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
  6. 2025T2-RAGBench: Text-and-Table Benchmark for Evaluating Retrieval-Augmented Generation
  7. 2025QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance
  8. 2025EviNote-RAG: Enhancing RAG Models via Answer-Supportive Evidence Notes