When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs

Abstract: Personalized LLMs adapt model behavior to individual users to enhance user satisfaction, yet personalization can inadvertently distort factual reasoning. We show that when personalized LLMs face factual queries, there exists a phenomenon where the model generates answers aligned with a user's prior history rather than the objective truth, resulting in personalization-induced hallucinations that degrade factual reliability and may propagate incorrect beliefs, due to representational entanglement between personalization and factual representations. To address this issue, we propose Factuality-Preserving Personalized Steering (FPPS), a lightweight inference-time approach that mitigates personalization-induced factual distortions while preserving personalized behavior. We further introduce PFQABench, the first benchmark designed to jointly evaluate factual and personalized question answering under personalization. Experiments across multiple LLM backbones and personalization methods show that FPPS substantially improves factual accuracy while maintaining personalized performance.

• The paper identifies personalization-induced hallucinations caused by latent representational entanglement, which distorts factual responses.
• It introduces the FPPS framework that applies layer selection, factuality probing, and adaptive steering to counteract hallucination risks.
• Empirical results using PFQABench show that FPPS, especially the mixed strategy (FPPS-M), significantly enhances factual accuracy while preserving personalization.

Hallucinations in Personalized LLMs: Mechanisms, Risks, and Countermeasures

Personalization-Induced Hallucinations: Failure Mode and Mechanistic Insight

The integration of personalization mechanisms into LLMs has led to tangible improvements in user alignment and satisfaction. However, this adaptation, typically achieved via mechanisms such as long-term memory injection or profile conditioning, introduces the risk of personalization-induced hallucinations. This distinct failure mode arises when the model, upon adapting to user-specific context, generates responses to factual queries that align with the user’s prior beliefs rather than objective truth, potentially reinforcing user misconceptions. (Figure 1)

Figure 1: Personalization biases factual QA in personalized LLMs; the proposed FPPS recovers factuality while retaining valid personalization.

The root cause is representational entanglement in the latent space. Personalization directions induced by user context are not orthogonal to those governing factual knowledge. When the two are entangled, internal model activations are shifted away from factual subspaces, impairing correct factual retrieval and yielding hallucinated outputs. Cosine similarity analysis of internal representations demonstrates that for hallucinated responses, the similarity between personalized and non-personalized representations drops sharply compared to truthful answers ($p < 0.001$) (Figure 2), supporting a representational rather than a surface-level decoding explanation of this failure.

Figure 2: Non-orthogonality of personalization directions leads to latent factual subspace distortion and decreased alignment on hallucinated responses.

Empirical Assessment: Knowledge Acquisition and Benchmarking

Simulation experiments reveal consequential downstream risk: user-facing LLMs, when personalized, systematically reduce factual learning efficacy of downstream users compared to standard models, with accuracy reductions averaging 10.5% in controlled teacher-student simulations. This effect is robust to model scale and induces epistemic loss that can accumulate over iterative use (Figure 3).

Figure 3: Personalized LLMs impair user factual knowledge acquisition in simulated pedagogical interactions.

To enable joint evaluation of personalization and factuality, the authors introduce PFQABench: a benchmark combining long-term user histories with multi-hop factual QA instances, organizing both factual and history-dependent queries per user (Figure 4). This resource supports fine-grained discrimination between personalization-induced failures and genuinely beneficial personalized responses.

Figure 4: PFQABench constructs session-aligned factual and personalized QA pairs for robust evaluation.

FPPS: The Factuality-Preserving Personalized Steering Framework

The proposed Factuality-Preserving Personalized Steering (FPPS) framework targets inference-time mitigation of hallucinations, aiming to retain valid personalization while suppressing factual compromise (Figure 5). FPPS is architected around three components:

1. Personalization-Sensitive Layer Selection: Identifies the internal model layer where personalization most strongly modulates factual predictions via perplexity deviation analysis across contrastive inputs.
2. Factuality Entanglement Prober: Trained at the identified layer to predict the degree to which the current hidden state reflects entangled factual-personalization risk, producing a control signal $\hat{p}$.
3. Adaptive Knowledge Steering Module: Depending on $\hat{p}$, applies either hard removal of personalized offsets (FPPS-H), soft vectorial steering toward factual subspaces (FPPS-S), or risk-adaptive mixed control (FPPS-M), balancing factuality and personalized benefits.

   Figure 5: The FPPS pipeline combines internal risk probing with adaptive steering at selected layers.

FPPS operates at inference with no additional retraining, making it practical and modular for deployment in open-weight LLMs.

Experimental Validation and Ablation

Across multiple LLM backbones (LLaMA3.1-8B-IT, Qwen2.5-7B/14B-IT) and personalization paradigms (retrieval and profile-based), FPPS variants yield substantial gains on PFQABench. FPPS-H achieves maximal factuality (F-Score), but often at personalization cost (P-Score). FPPS-S preserves more personalization but attains weaker hallucination mitigation. The FPPS-M mixed strategy consistently achieves the best balance, improving average overall performance by 50%+ versus base personalized models.

Ablation replacing either the factuality prober or steering vector with random alternatives severely degrades performance, demonstrating the necessity of structured, risk-aware internal modeling.

Model robustness is characterized as well; FPPS-M maintains stable factual QA performance even as more user history is retained, in contrast to strong monotonic factuality degradation in baseline personalized LLMs (Figure 6).

Figure 6: FPPS-M maintains factual QA performance regardless of injected user history, unlike baseline personalized models.

In pedagogical simulations, augmenting personalized teacher models with FPPS-M recovers the majority of the factual knowledge loss incurred by personalization, with a +7.0% average improvement over unmitigated personalized LLMs.

Implications, Limitations, and Future Directions

This work makes explicit the representational mechanism responsible for personalization-induced hallucinations and justifies selective, risk-aware correction rather than blanket suppression of personalization or hallucination. Its findings have direct applicability for the design of safe personalized LLMs in high-stakes environments (e.g., education, healthcare, decision support), emphasizing factual consistency as a first-class system constraint.

Notable limitations include a focus on open-weight LLMs due to representation access requirements, and reliance on PFQABench which, while well-constructed, does not cover the full social or longitudinal impact landscape of personalized factual learning. Extension to closed-source, API-based LLMs will likely require native support for internal risk probing, possibly via tailored architecture modifications or interpretability hooks.

Future research should address deploying internal factuality-aware steering in black-box and proprietary models, further scaling benchmarks to naturalistic and adversarial settings, and exploring the interaction of personalization, factual calibration, and epistemic trust at longer time horizons or larger user bases.

Conclusion

Personalized LLMs exhibit a unique vulnerability wherein user-aligned context distorts factual reasoning via latent representational entanglement, causing a systematic hallucination mode with downstream knowledge transmission risk. The FPPS framework offers a principled, modular solution: detecting entanglement in real time and adaptively steering internal states to restore factuality. Empirical gains on factual QA, stability under variable history, and mitigation of knowledge loss in user simulations demonstrate that factuality-preserving steering is feasible and essential for reliable personalized LLM deployment.