Analyzing the Correlation Between Hallucinations and Knowledge Conflicts in Large Language Models
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 One proposed explanation is internal knowledge conflicts arising from fixed, outdated training data.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. ...
| Authors | Lucrezia Laraspata, Giovanna Castellano, Gennaro Vessio |
|---|---|
| Primary category | cs.CL |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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