mlsanityData to 14 Oct 2026

E2E-AFG: An End-to-End Model with Adaptive Filtering for Retrieval-Augmented Generation

arXiv 2411.00437 ↗First posted 1 Nov 2024cs.CL cs.AIcited by 0

On the map: Retrieval-augmented generation → Generation and context integration

Abstract (arXiv)

Retrieval-augmented generation methods often neglect the quality of content retrieved from external knowledge bases, resulting in irrelevant information or potential misinformation that negatively affects the generation results of large language models. In this paper, we propose an end-to-end model with adaptive filtering for retrieval-augmented generation (E2E-AFG), which integrates answer existence judgment and text generation into a single end-to-end framework. This enables the model to focus more effectively on relevant content while reducing the influence of irrelevant information and generating accurate answers. We evaluate E2E-AFG on six representative knowledge-intensive language datasets, and the results show that it consistently outperforms baseline models across all tasks, demonstrating the effectiveness and robustness of the proposed approach.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation. Builds on 28 of its 35 references that are in the map. First posted on arXiv 1 Nov 2024.

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28 of 35 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023In-Context Retrieval-Augmented Language Models31 Jan
  2. 2023REPLUG: Retrieval-Augmented Black-Box Language Models30 Jan
  3. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Models5 Aug
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks22 May
  5. 2020REALM: Retrieval-Augmented Language Model Pre-Training10 Feb

Other references · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  5. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  6. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  7. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
  8. 2021Improving language models by retrieving from trillions of tokenscited by 1,293
  9. 2019Passage Re-ranking with BERTcited by 645
  10. 2019ELI5: Long Form Question Answeringcited by 575
  11. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 573
  12. 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
11 more
  1. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  2. 2023Query2doc: Query Expansion with Large Language Modelscited by 149
  3. 2021Decontextualization: Making Sentences Stand-Alonecited by 102
  4. 2023Learning to Filter Context for Retrieval-Augmented Generationcited by 83
  5. 2021Multi-Task Learning in Natural Language Processing: An Overviewcited by 62
  6. 2019Understanding the Behaviors of BERT in Rankingcited by 49
  7. 2021Evidentiality-guided Generation for Knowledge-Intensive NLP Taskscited by 40
  8. 2023SAIL: Search-Augmented Instruction Learningcited by 33
  9. 2024Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadatacited by 16
  10. 2020RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answeringcited by 9
  11. 2021Efficient Retrieval Optimized Multi-task Learningcited by 3

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  5. 2024Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models
  6. 2022A Survey on Retrieval-Augmented Text Generation
  7. 2026Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem
  8. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation