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

2026-06-07

AI Summary

This study investigates whether hallucinations in large language models (LLMs) on knowledge-intensive tasks stem from internal knowledge conflicts, with a particular focus on the impact of fixed and outdated training data. Employing probing techniques, the authors conduct a fine-grained analysis of activation patterns across hidden layers, attention heads, MLP sublayers, and output logits in LLaMA-3-8B and Falcon-7B. This work presents the first systematic comparison of the internal representations underlying hallucinatory behavior and knowledge conflict. The findings reveal that while these phenomena are correlated, they are not reducible to one another. Furthermore, the study demonstrates the generality and robustness of probing methods across multiple languages and model layers, offering an effective tool for enhancing the interpretability of large language models.

Abstract

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 One proposed explanation is internal knowledge conflicts arising from fixed, outdated training data.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.

Problem

• hallucinations
• knowledge conflicts
• large language models
• internal representations
• LLM interpretability

Innovation

• hallucination
• knowledge conflict
• probing
• large language models
• model interpretability