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Analyzing the Correlation Between Hallucinations and Knowledge Conflicts in Large Language Models

arXiv 2606.08705 ↗First posted 7 Jun 2026cs.CLcited by 0

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Abstract (arXiv)

Hallucinations -- factually incorrect or unverifiable outputs -- remain one of the most challenging limitations of Large Language Models (LLMs), especially in knowledge-intensive tasks. One proposed explanation is internal knowledge conflicts arising from fixed, outdated training data. This paper investigates whether internal representations linked to knowledge conflicts correlate with hallucination behaviors in LLMs. Using probing techniques inspired by two prior works, we analyzed activations from hidden, attention, and MLP layers, as well as output logits, across predefined tasks. We probed LLaMA-3-8B on hallucination detection benchmarks and Falcon-7B on a knowledge conflict dataset. Our findings show that, although conceptually related, hallucination activation patterns cannot be fully reduced to or explained by knowledge conflict representations. Nonetheless, probing proves a robust tool across multiple languages and activation types, supporting its role in improving LLM interpretability. This work advances the broader understanding of hallucinations in LLMs and underscores the value of fine-grained analysis of their internal behavior.

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References · 19

  1. 2024The Llama 3 Herd of Modelscited by 14,192
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  10. 2023Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflictscited by 333
  11. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Modelscited by 329
  12. 2023Physics of Language Models: Part 3.1, Knowledge Storage and Extractioncited by 264
7 more
  1. 2024Seven Failure Points When Engineering a Retrieval Augmented Generation Systemcited by 138
  2. 2024The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Modelscited by 87
  3. 2023Physics of Language Models: Part 1, Learning Hierarchical Language Structurescited by 83
  4. 2024Lynx: An Open Source Hallucination Evaluation Modelcited by 55
  5. 2023On Early Detection of Hallucinations in Factual Question Answeringcited by 54
  6. 2025SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared Task on Hallucinations and Related Observable Overgeneration Mistakescited by 27
  7. 2024Analysing the Residual Stream of Language Models Under Knowledge Conflictscited by 15

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