mlsanityData to 14 Oct 2026

ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generation

arXiv 2410.15511 ↗First posted 20 Oct 2024cs.IRcited by 4

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

Abstract (arXiv)

Open-domain long-form text generation requires generating coherent, comprehensive responses that address complex queries with both breadth and depth. This task is challenging due to the need to accurately capture diverse facets of input queries. Existing iterative retrieval-augmented generation (RAG) approaches often struggle to delve deeply into each facet of complex queries and integrate knowledge from various sources effectively. This paper introduces ConTReGen, a novel framework that employs a context-driven, tree-structured retrieval approach to enhance the depth and relevance of retrieved content. ConTReGen integrates a hierarchical, top-down in-depth exploration of query facets with a systematic bottom-up synthesis, ensuring comprehensive coverage and coherent integration of multifaceted information. Extensive experiments on multiple datasets, including LFQA and ODSUM, alongside a newly introduced dataset, ODSUM-WikiHow, demonstrate that ConTReGen outperforms existing state-of-the-art RAG models.

Key facts. Cited by 4 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 27 of its 35 references that are in the map. First posted on arXiv 20 Oct 2024.

Builds on

27 of 35 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct

Other references · 26

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  5. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
  6. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  7. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  8. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  9. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  10. 2019ELI5: Long Form Question Answeringcited by 575
  11. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 573
  12. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 532
14 more
  1. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  2. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 346
  3. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 318
  4. 2022Towards a Unified Multi-Dimensional Evaluator for Text Generationcited by 264
  5. 2022ASQA: Factoid Questions Meet Long-Form Answerscited by 234
  6. 2018WikiHow: A Large Scale Text Summarization Datasetcited by 204
  7. 2021Hurdles to Progress in Long-form Question Answeringcited by 167
  8. 2020Answering Complex Open-Domain Questions with Multi-Hop Dense Retrievalcited by 162
  9. 2023Walking Down the Memory Maze: Beyond Context Limit through Interactive Readingcited by 133
  10. 2023Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Taskscited by 73
  11. 2019Answering Complex Open-domain Questions Through Iterative Query Generationcited by 60
  12. 2022Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrievalcited by 20
  13. 2021Exploiting Reasoning Chains for Multi-hop Science Question Answeringcited by 15
  14. 2021HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articlescited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.

  1. 2025SurGE: A Benchmark and Evaluation Framework for Scientific Survey Generationcited by 16
  2. 2026PruneRAG: Confidence-Guided Query Decomposition Trees for Efficient Retrieval-Augmented Generationcited by 5
  3. 2024A Survey of Query Optimization in Large Language Modelscited by 1
  4. 2026Disco-RAG: Discourse-Aware Retrieval-Augmented Generationcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Dynamic Context Selection for Retrieval-Augmented Generation: Mitigating Distractors and Positional Bias
  2. 2024Retrieval is Accurate Generation
  3. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  4. 2025On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation Systems
  5. 2025Aligning Extraction and Generation for Robust Retrieval-Augmented Generation
  6. 2025From Retrieval to Generation: Comparing Different Approaches
  7. 2025Context-Guided Dynamic Retrieval for Improving Generation Quality in RAG Models
  8. 2026Bridge-RAG: An Abstract Bridge Tree Based Retrieval Augmented Generation Algorithm