mlsanityData to 14 Oct 2026

Tug-of-War within A Decade: Conflict Resolution in Vulnerability Analysis via Teacher-Guided Retrieval-Augmented Generations

arXiv 2604.14172 ↗First posted 25 Mar 2026cs.CL cs.AIcited by 0

On the map: Retrieval-augmented generation → Privacy and security of RAG

Abstract (arXiv)

Large Language Models (LLMs) are essential for analyzing and addressing vulnerabilities in cybersecurity. However, among over 200,000 vulnerabilities were discovered in the past decade, more than 30,000 have been changed or updated. This necessitates frequent updates to the training datasets and internal knowledge bases of LLMs to maintain knowledge consistency. In this paper, we focus on the problem of knowledge discrepancy and conflict within CVE (Common Vulnerabilities and Exposures) detection and analysis. This problem hinders LLMs' ability to retrieve the latest knowledge from original training datasets, leading to knowledge conflicts, fabrications of factually incorrect results, and generation hallucinations. To address this problem, we propose an innovative two-stage framework called CRVA-TGRAG (Conflict Resolution in Vulnerability Analysis via Teacher-Guided Retrieval-Augmented Generation). First, to improve document retrieval accuracy during the retrieval stage, we utilize Parent Document Segmentation and an ensemble retrieval scheme based on semantic similarity and inverted indexing. Second, to enhance LLMs' capabilities based on the retrieval of CVE dataset in generation stage, we employ a teacher-guided preference optimization technique to fine-tune LLMs. Our framework not only enhances the quality of content retrieval through RAG but also leverages the advantages of preference fine-tuning in LLMs to answer questions more effectively and precisely. Experiments demonstrate our method achieves higher accuracy in retrieving the latest CVEs compared to external knowledge bases. In conclusion, our framework significantly mitigates potential knowledge conflicts and inconsistencies that may arise from relying solely on LLMs for knowledge retrieval.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation. Builds on 24 of its 32 references that are in the map. First posted on arXiv 25 Mar 2026.

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24 of 32 references.

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Same direction, newest first

  1. 2024ProveRAG: Provenance-Driven Vulnerability Analysis with Automated Retrieval-Augmented LLMs22 Oct
  2. 2024Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions25 Feb

Other references · 22

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  6. 2019Language Models as Knowledge Bases?cited by 1,936
  7. 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 719
  8. 2024DeepSeek LLM: Scaling Open-Source Language Models with Longtermismcited by 642
  9. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 451
  10. 2023Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflictscited by 341
  11. 2021Time-Aware Language Models as Temporal Knowledge Basescited by 296
  12. 2024Knowledge Conflicts for LLMs: A Surveycited by 283
10 more
  1. 2021Towards Continual Knowledge Learning of Language Modelscited by 174
  2. 2024Tug-of-War Between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Modelscited by 93
  3. 2024Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Modelscited by 91
  4. 2023Do Large Language Models Know about Facts?cited by 51
  5. 2023Resolving Knowledge Conflicts in Large Language Modelscited by 50
  6. 2024RAG-Fusion: a New Take on Retrieval-Augmented Generationcited by 42
  7. 2024Adversarial Search Engine Optimization for Large Language Modelscited by 40
  8. 2023Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledgecited by 32
  9. 2024Using LLMs to Automate Threat Intelligence Analysis Workflows in Security Operation Centerscited by 10
  10. 2024A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoningcited by 8

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  3. 2025ReVul-CoT: Towards Effective Software Vulnerability Assessment with Retrieval-Augmented Generation and Chain-of-Thought Prompting
  4. 2025LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis
  5. 2024CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity Knowledge
  6. 2025Automated Vulnerability Validation and Verification: A Large Language Model Approach
  7. 2025CTIConnect: A Benchmark for Retrieval-Augmented LLMs over Heterogeneous Cyber Threat Intelligence
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