mlsanityData to 14 Oct 2026

R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation

arXiv 2410.20598 ↗First posted 27 Oct 2024cs.IRcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Retrieval-augmented generation (RAG) has gained wide attention as the key component to improve generative models with external knowledge augmentation from information retrieval. It has shown great prominence in enhancing the functionality and performance of large language model (LLM)-based applications. However, with the comprehensive application of RAG, more and more problems and limitations have been identified, thus urgently requiring further fundamental exploration to improve current RAG frameworks. This workshop aims to explore in depth how to conduct refined and reliable RAG for downstream AI tasks. To this end, we propose to organize the first R3AG workshop at SIGIR-AP 2024 to call for participants to re-examine and formulate the basic principles and practical implementation of refined and reliable RAG. The workshop serves as a platform for both academia and industry researchers to conduct discussions, share insights, and foster research to build the next generation of RAG systems. Participants will engage in discussions and presentations focusing on fundamental challenges, cutting-edge research, and potential pathways to improve RAG. At the end of the workshop, we aim to have a clearer understanding of how to improve the reliability and applicability of RAG with more robust information retrieval and language generation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 18 of its 22 references that are in the map. First posted on arXiv 27 Oct 2024.

Builds on

18 of 22 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Retrieval-Augmented Generation-based Relation Extraction20 Apr
  2. 2024The Power of Noise: Redefining Retrieval for RAG Systems26 Jan

Other references · 16

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2023GPT-4 Technical Reportcited by 18,396
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  6. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  7. 2021Improving language models by retrieving from trillions of tokenscited by 1,293
  8. 2023Editing Large Language Models: Problems, Methods, and Opportunitiescited by 372
  9. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  10. 2023Benchmarking Large Language Models in Retrieval-Augmented Generationcited by 312
  11. 2023Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Modelscited by 184
  12. 2023Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answeringcited by 151
4 more
  1. 2024Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Basescited by 77
  2. 2024RaFe: Ranking Feedback Improves Query Rewriting for RAGcited by 49
  3. 2023Knowledge-Augmented Language Model Verificationcited by 22
  4. 2023Influence of External Information on Large Language Models Mirrors Social Cognitive Patternscited by 16

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