mlsanityData to 14 Oct 2026

Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG

arXiv 2410.02825 ↗First posted 30 Sep 2024cs.CL cs.CRcited by 2

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Abstract (arXiv)

This paper presents new methods that have the potential to improve privacy process efficiency with LLM and RAG. To reduce hallucination, we continually pre-train the base LLM model with a privacy-specific knowledge base and then augment it with a semantic RAG layer. Our evaluations demonstrate that this approach enhances the model performance (as much as doubled metrics compared to out-of-box LLM) in handling privacy-related queries, by grounding responses with factual information which reduces inaccuracies.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 12 references that are in the map. First posted on arXiv 30 Sep 2024.

Builds on

8 of 12 references.

References · 8

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  4. 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Themcited by 1,807
  5. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
  6. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
  7. 2023Using an LLM to Help With Code Understandingcited by 228
  8. 2023How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrievalcited by 118

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Cited by year (2026 to 14 Oct)

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

2 papers.

  1. 2025Compliance Brain Assistant: Conversational Agentic AI for Assisting Compliance Tasks in Enterprise Environmentscited by 4
  2. 2026Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR)cited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Differentially Private Retrieval-Augmented Generation
  2. 2025DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation
  3. 2024Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs
  4. 2024"Ghost of the past": identifying and resolving privacy leakage from LLM's memory through proactive user interaction
  5. 2025LLM-as-a-Judge for Privacy Evaluation? Exploring the Alignment of Human and LLM Perceptions of Privacy in Textual Data
  6. 2024RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbots
  7. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
  8. 2026SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systems