mlsanityData to 7 Oct 2026

Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs

arXiv 2408.05524 ↗First posted 10 Aug 2024cs.CL cs.DBcited by 5

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

Abstract (arXiv)

Retrieval-Augmented Large Language Models (RALMs) have made significant strides in enhancing the accuracy of generated responses.However, existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods.We propose to boost the precision of RALMs' answers from a data quality perspective through the Context-Driven Index Trimming (CDIT) framework, where Context Matching Dependencies (CMDs) are employed as logical data quality rules to capture and regulate the consistency between retrieved contexts.Based on the semantic comprehension capabilities of Large Language Models (LLMs), CDIT can effectively identify and discard retrieval results that are inconsistent with the query context and further modify indexes in the database, thereby improving answer quality.Experiments demonstrate on challenging question-answering tasks.Also, the flexibility of CDIT is verified through its compatibility with various language models and indexing methods, which offers a promising approach to bolster RALMs' data quality and retrieval precision jointly.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation. Builds on 25 of its 58 references that are in the map. First posted on arXiv 10 Aug 2024.

Builds on

25 of 58 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023REPLUG: Retrieval-Augmented Black-Box Language Models30 Jan
  2. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories20 Dec
  3. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Models5 Aug
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks22 May

Other references · 21

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  6. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  7. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  8. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  9. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  10. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  11. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  12. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
9 more
  1. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 853
  2. 2023AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedbackcited by 738
  3. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 719
  4. 2023The Falcon Series of Open Language Modelscited by 526
  5. 2023Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-Incited by 71
  6. 2023RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledgecited by 51
  7. 2023Synergistic Interplay between Search and Large Language Models for Information Retrievalcited by 11
  8. 2021Efficient Discovery of Approximate Order Dependenciescited by 2
  9. 2016Finding Influential Users in Social Media Using Association Rule Learningcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026TEMA: Anchor the Image, Follow the Text for Multi-Modification Composed Image Retrievalcited by 13
  2. 2026Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generationcited by 5
  3. 2026CAST: Character-and-Scene Episodic Memory for Agentscited by 3
  4. 2026Context-Agent: Dynamic Discourse Trees for Non-Linear Dialoguecited by 1
  5. 2026Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Studycited by 0

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  5. 2024Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?
  6. 2025SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression
  7. 2024Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models
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