mlsanityData to 7 Oct 2026

Context Awareness Gate For Retrieval Augmented Generation

arXiv 2411.16133 ↗First posted 25 Nov 2024cs.LG cs.IRcited by 1

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

Abstract (arXiv)

Retrieval Augmented Generation (RAG) has emerged as a widely adopted approach to mitigate the limitations of large language models (LLMs) in answering domain-specific questions. Previous research has predominantly focused on improving the accuracy and quality of retrieved data chunks to enhance the overall performance of the generation pipeline. However, despite ongoing advancements, the critical issue of retrieving irrelevant information -- which can impair the ability of the model to utilize its internal knowledge effectively -- has received minimal attention. In this work, we investigate the impact of retrieving irrelevant information in open-domain question answering, highlighting its significant detrimental effect on the quality of LLM outputs. To address this challenge, we propose the Context Awareness Gate (CAG) architecture, a novel mechanism that dynamically adjusts the LLMs' input prompt based on whether the user query necessitates external context retrieval. Additionally, we introduce the Vector Candidates method, a core mathematical component of CAG that is statistical, LLM-independent, and highly scalable. We further examine the distributions of relationships between contexts and questions, presenting a statistical analysis of these distributions. This analysis can be leveraged to enhance the context retrieval process in Retrieval Augmented Generation (RAG) systems.

Key facts. Cited by 1 paper 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 26 of its 27 references that are in the map. First posted on arXiv 25 Nov 2024.

Builds on

26 of 27 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models9 Oct
  2. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation31 Mar
  3. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity21 Mar
  4. 2024Tug-of-War Between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models22 Feb
  5. 2023Benchmarking Large Language Models in Retrieval-Augmented Generation4 Sep

Other references · 21

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  6. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  7. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  8. 2024Qwen2 Technical Reportcited by 2,076
  9. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,928
  10. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  11. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  12. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
9 more
  1. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 317
  2. 2023Query Rewriting for Retrieval-Augmented Large Language Modelscited by 211
  3. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 200
  4. 2022Scaling Up Models and Data with textttt5x and textttseqiocited by 187
  5. 2023Take a Step Back: Evoking Reasoning via Abstraction in Large Language Modelscited by 157
  6. 2023Query2doc: Query Expansion with Large Language Modelscited by 146
  7. 2024Certifiably Robust RAG against Retrieval Corruptioncited by 144
  8. 2024Searching for Best Practices in Retrieval-Augmented Generationcited by 83
  9. 2023Large Language Model based Long-tail Query Rewriting in Taobao Searchcited by 61

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Beyond Code Snippets: Benchmarking LLMs on Repository-Level Question Answeringcited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2025Dynamic Context Selection for Retrieval-Augmented Generation: Mitigating Distractors and Positional Bias
  2. 2025On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation Systems
  3. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
  4. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices
  5. 2024Better RAG using Relevant Information Gain
  6. 2025On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation
  7. 2024In Defense of RAG in the Era of Long-Context Language Models
  8. 2024RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation