💬 NLP

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

This paper investigates the correlation between knowledge conflicts and hallucinations in Large Language Models by analyzing internal representations across different layers, finding that while the two phenomena are conceptually related, hallucination patterns cannot be fully explained by knowledge conflict representations, thereby highlighting the value of fine-grained interpretability tools for understanding LLM behavior.

Original authors: Lucrezia Laraspata, Giovanna Castellano, Gennaro Vessio

Published 2026-06-09

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CC BY 4.0

Original authors: Lucrezia Laraspata, Giovanna Castellano, Gennaro Vessio

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine Large Language Models (LLMs) as incredibly well-read librarians who have memorized a massive library of books. However, there's a catch: they can't update their shelves. If a book in their library is outdated or if they are handed a new note that contradicts what they've memorized, they might get confused.

This paper investigates two specific ways this confusion can go wrong:

The "Hallucination": The librarian confidently makes up a fact or gives an answer that sounds real but is actually wrong.
The "Knowledge Conflict": The librarian is handed a note (context) that says "The sky is green," but their memory says "The sky is blue." They have to decide which one to trust.

The Big Question

The researchers wanted to know: Are these two problems the same thing?

They suspected that when a librarian "hallucinates," it's because they are internally fighting a "knowledge conflict." In other words, they thought the librarian's brain was buzzing with confusion (conflict) right before they made up a lie (hallucination). If this were true, we could build a simple alarm system: if we detect the "confusion buzz," we know a lie is coming.

How They Tested It

To test this, the researchers acted like "brain scanners" (a technique called probing). They didn't change the librarians; they just peeked inside their heads at different stages of thinking (the hidden layers, the attention layers, and the logic layers) to see if they could spot the signs of confusion or lying.

They used two different "librarians" (AI models):

LLaMA-3-8B: They tried to see if this librarian's "confusion signals" could predict when it would lie.
Falcon-7B: They tried to see if this librarian's "lying signals" could predict when it was confused.

What They Found

The results were a bit surprising. It's like checking a smoke detector and finding that the alarm for "fire" doesn't actually go off when someone burns toast, and the alarm for "burning toast" doesn't go off when a fire starts.

Confusion $\neq$ Lying: When they looked at the "confusion" signals inside the model, they couldn't use them to predict when the model would hallucinate. The internal patterns of being confused were different from the patterns of making things up.
Lying $\neq$ Confusion: Conversely, the signals that usually indicate the model is lying didn't help them spot when the model was facing a knowledge conflict.

The Takeaway: Even though it feels like hallucinations are caused by knowledge conflicts, the paper shows that inside the model's "brain," these are actually two different processes. You can't just look for one to find the other. They are distinct phenomena.

One Good News

While they didn't find the link they were looking for, they did find that their "brain scanner" (the probing tool) works very consistently. It works just as well in English as it does in Italian, Spanish, Chinese, and many other languages. This means the tool itself is reliable, even if the specific theory about the link between conflict and hallucination wasn't proven.

In Short

The researchers hoped to find a single "smoke detector" that could catch both the confusion and the lying in AI models. They found that the AI's brain handles these two issues differently. While they are related in the real world, inside the computer code, they don't look the same. This tells us we need more complex tools to understand and fix AI mistakes, rather than just looking for one simple cause.

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

Problem Statement
Large Language Models (LLMs) face significant limitations in knowledge-intensive tasks, primarily due to their fixed, static training data which cannot be updated post-training. When models encounter information outside their parametric knowledge or conflicting with it, they often generate "hallucinations"—factually incorrect, inconsistent, or unverifiable outputs. A prevailing hypothesis suggests that these hallucinations stem from internal knowledge conflicts, where the model's stored parametric knowledge clashes with contextual cues provided in the input. However, the specific relationship between the internal representations of knowledge conflicts and hallucination behaviors remains under-explored. While prior works have successfully developed probing techniques to detect knowledge conflicts (Zhao et al.) and hallucinations (Snyder et al.) independently, it is unclear whether these two phenomena share a common internal encoding or if one can be used to predict the other.

Methodology and Experimental Design
This study employs a probing-based framework to investigate the bidirectional correlation between knowledge conflicts and hallucinations. The authors hypothesize that if hallucinations are caused by knowledge conflicts, the internal activation patterns of a hallucinated output should be detectable by a classifier trained to identify knowledge conflicts, and vice versa.

The experimental design involves two distinct directions:

From Knowledge Conflicts to Hallucinations:
- Model: LLaMA-3-8B.
- Training: Knowledge conflict probing models ( $KC_{a,i}$ ) were trained using Logistic Regression on the NQ-Swap dataset (a synthetic dataset of conflicting QA pairs) to detect conflict signals in hidden, attention, and MLP layer activations.
- Evaluation: These trained conflict detectors were applied to activations extracted from Mu-SHROOM, HaluEval, and HaluBench (datasets containing hallucinated QA pairs). The goal was to determine if the conflict detector could accurately predict the presence of hallucinations.
From Hallucinations to Knowledge Conflicts:
- Model: Falcon-7B.
- Training: Hallucination probing models ( $H_l, H_{a,i}$ ) were trained using feed-forward classifiers on the TriviaQA dataset to detect hallucination signals in output logits and intermediate activations (attention and MLP).
- Evaluation: These hallucination detectors were applied to the NQ-Swap dataset to determine if the presence of a knowledge conflict could be predicted by the hallucination detector.

Key Results
The empirical analysis yielded the following findings:

Lack of Strong Correlation: Contrary to the hypothesis that hallucinations are directly reducible to knowledge conflicts, the study found no significant correlation between the internal representations of the two phenomena.
- When probing hallucinations using knowledge conflict models (LLaMA-3-8B), accuracy fluctuated but remained low (often near chance levels, with AUROC close to 0.5). Notably, performance declined in the intermediate layers (15–25) where knowledge conflicts are typically detected most precisely in isolation.
- Conversely, when probing knowledge conflicts using hallucination models (Falcon-7B), the detectors performed at chance levels (accuracy $\approx$ 0.5, AUROC $\approx$ 0.5) across all layers. This contrasts with the reference study by Snyder et al., where hallucination detection improved in later layers; here, the trend was negligible.
Activation Specificity: The study observed that hidden activations in LLaMA-3-8B were less informative for conflict detection than attention or MLP activations. In Falcon-7B, the opposite trend was observed compared to prior literature, with attention activations sometimes outperforming MLPs in this specific cross-task context.
Robustness Across Languages: Despite the lack of correlation between the phenomena, the probing methodology itself proved robust. When applied to the multilingual Mu-SHROOM dataset (covering 14 languages), the probing models maintained stable performance across different languages, indicating that the technique is not heavily biased toward specific linguistic structures.

Significance and Contributions
The paper contributes to the broader understanding of LLM interpretability by providing systematic empirical evidence that hallucinations and knowledge conflicts are not strongly aligned at the level of internal representations.

Decoupling Phenomena: The findings challenge the intuitive assumption that hallucinations are solely or primarily driven by internal knowledge conflicts. This suggests that hallucinations may arise from more complex, orthogonal mechanisms that are not fully captured by conflict detection signals alone.
Methodological Validation: While the correlation hypothesis was not supported, the study validates the utility of probing as a robust tool for analyzing internal model behavior across different activation types (hidden, attention, MLP) and languages.
Future Directions: The authors emphasize the need to decouple these phenomena in future research to develop more targeted mitigation strategies. They propose extending these probing methodologies to high-stakes domains such as digital health, education, and human resource management, while noting the critical importance of ethical considerations regarding bias and misinterpretation of probing results in multilingual and sensitive contexts.

The source code for the reproduction of these experiments is publicly available, facilitating further investigation into the internal mechanics of LLMs.