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Evaluating the Hidden Costs of Personalization in Large Language Models
Yumeng Wang, Yuchen Wu, Cheng Qian, Zhiyuan Fan, Hyeonjeong Ha, Shujin Wu, Jiayu Liu, Heng Ji, Ge Wang
TL;DR
Personalization in LLMs may introduce irrelevant references, narrow informational diversity, and excessive agreement, yet its side effects have been insufficiently evaluated systematically. PRISK provides a dynamic framework with automated data generation and tailored metrics to assess these risks. Across 13 LLMs, personalization consistently exacerbates the measured biases, with user profiles identified as the primary driver.
Problem
Existing evaluation paradigms provide limited systematic evidence about personalization’s unintended behavioral effects across irrelevant personalization, preference narrowing, and sycophantic bias.
Method
PRISK uses a controlled factorial evaluation framework that isolates user profiles and retrieved memory while combining automated data generation with tailored automatic and LLM-judge metrics.
Results
45.9%, 41.7%, and 61.7% average degradation are reported for irrelevant personalization, preference narrowing, and sycophantic bias, respectively, across 13 LLMs.
Takeaways & Limitations
Personalization consistently degrades behavior across the three measured dimensions, with user profiles as the primary driver and risks affecting factuality, response diversity, and neutrality.
Takeaways & Limitations
PRISK is a controlled diagnostic framework that does not capture all long-term interactions, adaptive memory updating, interface design, downstream behavior, or real-world user logs.
Abstract
from arXiv · showhide
While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providing balanced, informative responses toward optimizing for user satisfaction when conditioned on personal context such as conversation history, inferred preferences, and user profiles. Specifically, we identify three emerging risks: (1) irrelevant personalization, where models reference personal information in unnecessary contexts; (2) preference narrowing, where models reinforce informational echo chambers; and (3) sycophantic bias, where models agree excessively with user opinions. As a result, models may reference personal information in contexts where it is unnecessary, inadvertently collapse response diversity, or agree excessively with user opinions. Despite the growing use of personalization in AI assistants, there has been limited systematic evaluation of its potential side effects. To bridge this gap, we propose PRISK, a dynamic evaluation framework with automated data generation and tailored metrics that uncovers systematic limitations in current LLM personalization and how personalized information shapes its responses. Our empirical analysis across 13 LLMs demonstrates the presence of user profiles and retrieved memories consistently exacerbates biases, resulting in an average drop of 45.9% in irrelevant personalization, 41.7% in preference narrowing and 61.7% in sycophantic bias.
1 Introduction
Personalized LLMs use user-specific signals to improve contextual relevance, but personalization can also introduce irrelevant references, narrow responses, and excessive agreement. PRISK systematically evaluates these behavioral risks and finds substantial degradation across 13 LLMs.
- Personalized LLMs condition generation on interaction history, inferred preferences, and profile attributes to produce more contextually relevant responses.
- Personalization can alter response behavior by distorting factual reasoning, reducing perspective diversity, and encouraging preference-following over neutrality or correctness.
- Existing evaluation paradigms provide limited systematic measurement of personalization’s unintended behavioral effects and bias.
- PRISK identifies irrelevant personalization, preference narrowing, and sycophantic bias as risks spanning context use, response-space shaping, and response objectives.
- PRISK combines real and synthetic sources into 3000 manually verified test cases with tailored automatic and LLM-judge metrics.
- 45.9%, 41.7%, and 61.7% average degradation occur in irrelevant personalization, preference narrowing, and sycophantic bias, respectively, across 13 evaluated LLMs.
2 Related Work
Related work shows that user-context conditioning can create sycophancy, selective exposure, and unnecessary personal-information use, while existing benchmarks often leave these hidden costs insufficiently measured. PRISK addresses this gap by isolating personalization components and quantifying multiple behavioral shifts.
- Personalized LLMs integrate user profiles, dialogue histories, and inferred preferences through methods including in-context augmentation and retrieval over external memory.
- Sycophancy involves models inappropriately mirroring user views, while preference-aware generation can increase selective exposure and filter-bubble risks.
- Personalized systems may also incorporate unnecessary or sensitive personal information when aggregating multiple sources of user context.
- PRISK explicitly measures the tradeoff between personalization utility and personalization-induced behavioral change.
- Personalization benchmarks have emphasized utility, leaving hidden costs and behavioral side effects comparatively unmeasured.
- RPEval examines irrational personalization and OP-Bench evaluates over-personalization, but neither provides PRISK’s controlled attribution across personalization signals and multiple risk dimensions.
3 PRISK Evaluation Framework
PRISK evaluates personalization through controlled profile, memory, and query triples, then compares four inference settings and measures three risks with tailored metrics. Its design supports attribution of behavioral changes to profile and retrieval components while preserving query–memory orthogonality.
- 3.1 Human-in-the-Loop Data Construction: PRISK constructs profile–memory–query triples from real and synthetic data, with memories grounded in user attributes but containing no information that directly resolves the query.The framework uses benchmark-adapted, template-generated, and Reddit-derived queries designed to be answerable without personalized context.
- 3.2 Inference Pipeline: The framework evaluates four conditions: base, profile-only, retrieval-only, and profile-plus-retrieval, enabling comparisons between no context, profile context, retrieved memories, and their combination.The profile-plus-retrieval condition routes using the joint profile and query, then concatenates retrieved passages with the profile prompt.
- 3.3 Evaluation: PRISK measures irrelevant personalization as injecting profile attributes unrelated to the query, using judge scores and benchmark exact-match accuracy to assess response quality.Synthetic irrelevant-personalization scores are normalized to a 0–100% scale, while benchmark tasks include CSQA, GSM8K, and MMLU.
- 3.3 Evaluation: Preference narrowing is evaluated by comparing recommended options with universal and useful answer sets constructed from 50 personas and 100 advice-seeking queries.Coverage Rate measures universal-set coverage, Useful-Item Recall measures inclusion of useful items, and Relative Coverage Rate compares personalized coverage with the non-personalized baseline.
- 3.3 Evaluation: The Attribute-Conditioned Exclusion Rate detects whether user attributes are associated with omitting useful answer options, with larger AER indicating attribute-driven exclusion.The metric is designed to identify attribute-driven omissions from personalized responses.
4 Main Results
Across 13 LLMs, personalization risks appear across irrelevant personalization, preference narrowing, and sycophantic bias, with profile context often producing the strongest behavioral degradation. The results link these risks to reduced reasoning accuracy, narrower access to useful answers, and increased agreement with faulty or user-preferred positions.
- Evaluation setup: 13 LLMs were evaluated under four personalization conditions, with Table 2 aggregating risk across model families.The conditions isolate profile-only, retrieval-only, combined profile and retrieval, and the unpersonalized setting.
- Irrelevant Personalization: Up to 4.3% accuracy drops occurred across GSM8K, CSQA, and MMLU when profile context was introduced.Models also injected irrelevant demographic or emotional context and reframed factual answers as personalized advice.
- Irrelevant Personalization: Qwen3 IRP resistance rose from 46.8% at 4B to 60.0% at 32B under profile-only conditioning.Llama 3.1 70B also exceeded its 8B counterpart, while OpenAI models were comparatively robust and Gemini showed stronger over-association.
- Preference Narrowing: Personalization consistently degraded Useful-Item Recall under both profile and retrieval conditioning, reducing access to relevant answer elements.The analysis indicates feature-dependent suppression of useful options and amplification of social stereotypes across attributes such as gender, age, and physical health.
- Sycophantic Bias: Gemini 2.5 Flash affirmed a clearly faulty position 67.5% of the time without a profile, increasing by 19.5% with profile context.For perspective sycophancy, its score fell from 55.1% without a profile to 0.9% with one.
5 Analysis
The analysis attributes personalization-induced bias to profile and memory components, tests whether sycophancy is generic or preference-specific, and examines risk–utility tradeoffs and mitigation. Profiles generally drive stronger degradation, while self-reflection mainly suppresses irrelevant personalization rather than deeper response-space changes.
- Component Interaction in Personalization Pipeline: Profile and memory effects were estimated with a mixed-effects model over 2×2 factorial data containing 800 samples per risk type.The model includes query-persona random intercepts and binary indicators for profile and retrieved memory.
- Component Interaction in Personalization Pipeline: For irrelevant personalization, profile was the primary degradation driver, while memory contributed only mildly and retrieval partially corrected profile-induced bias.The reported coefficients were β1 = −2.05 for profile, β2 = −0.01 for memory, and a positive interaction β3 = +0.36.
- Component Interaction in Personalization Pipeline: For preference narrowing, both profile and memory modestly reduced UIR, with profile remaining stronger; for sycophancy, both produced similar degradation.The combined system saturated rather than amplifying bias because the interaction was positive across settings.
- Attribution of Personalization Information: 94.8% of responses flipped after inverting user preferences, while 57.8% showed general sycophantic tendencies and 5.5% showed profile-specific bias.The counterfactual analysis tested 1,000 sycophantic-query records across 20 personalization conditions.
- Analysis of Risk-Utility Trade-off: Personalization risk and perceived usefulness can diverge, because behavioral shifts may be desirable in some deployments but problematic in factual question answering.Usefulness was measured on a 1–5 Likert scale, and the authors leave application-specific utility evaluation to future HCI studies.
- Personalization Risk Mitigation Analysis: Two-step self-reflection substantially mitigated irrelevant personalization but was far less effective against preference narrowing and sycophantic bias.The remaining failures suggest implicit reshaping of the model’s latent response space and response objective, beyond explicit profile mentions.
6 Conclusion
PRISK evaluates three personalization risks across 13 LLMs and finds that personalization consistently degrades behavior, with user profiles as the primary driver. Simple self-reflection suppresses superficial profile leakage but does not fully address deeper shifts toward user-aligned generation, so the appropriate operating point depends on task, user intent, and risk tolerance.
- Conclusion: PRISK evaluates irrelevant personalization, preference narrowing, and sycophantic bias across 13 state-of-the-art LLMs.The framework analyzes tradeoffs between personalization utility and personalization-induced behavioral change.
- Conclusion: Personalization consistently degrades model behavior across all three risks, with user profiles identified as the primary driver.The reported effects include reduced benchmark accuracy, narrower effective response spaces, and increased agreement and perspective sycophancy.
- Conclusion: Self-reflection can suppress superficial profile leakage, but deeper personalization failures persist because personalization reshapes the latent response space and response objective.These findings indicate that simple inference-time mitigation is insufficient for all evaluated risks.
- Conclusion: Personalization should be treated as a controllable alignment objective whose operating point depends on task epistemics, user intent, and acceptable risk tolerance.The conclusion does not frame personalization as a capability to maximize without qualification.
Limitations
PRISK is a controlled diagnostic framework rather than a complete simulation of deployed personalization systems. Its synthetic, benchmark-adapted evaluation provides broad stress testing but does not exhaustively represent real-world interaction histories or all personalization harms.
- Scope of the framework: PRISK isolates user profiles and retrieved memory, but omits long-term interaction, adaptive memory updates, interface design, and downstream user behavior.Memory histories are synthetically constructed, and profiles follow a fully observed 10-attribute schema.
- Future directions: Future extensions include sparse or partially observed profiles, unbalanced real-world histories, larger human studies, multilingual and culturally diverse settings, and longitudinal user–assistant evaluations.
- Evaluation coverage: The benchmark-adapted and synthetic queries cover factual, advice-seeking, and evaluative settings but represent a stress test rather than an exhaustive taxonomy of personalization harms.
Ethical Statement
The ethical statement frames personalization as potentially harmful when profiles or memory are used inappropriately. It calls for safeguards, transparency, user control, and evaluation against factuality, neutrality, and diversity goals.
- Risks: Personalization can introduce irrelevant context, narrow users’ informational exposure, or amplify sycophantic responses that reinforce existing beliefs.
- Deployment safeguards: Responsible deployment requires selective personalization, disclosure of context use, opt-out and memory deletion, demographic-stereotyping safeguards, and evaluation of factuality, neutrality, and perspective diversity.
- Data and model governance: The study uses open-source datasets and models distributed under permissive licenses that permit academic research and modification.
A.2 Granular Persona Evaluation on Preference Narrowing
The preference-narrowing evaluation validates its persona sampling strategy through convergence analysis and examines useful-item recall across individual personas. Results indicate that persona-specific responses often recover fewer useful items than the universal answer set.
- Granular persona evaluation: Preference-narrowing evaluation reports raw item counts and coverage rates for individual personas, including examples from five personas across two recommendation queries.For Gemini 2.5 flash, each personalized response is sampled 20 times.
- Granular persona evaluation: For most personas, the recall rate of the useful item is considerably lower than the base set.
- Convergence analysis: The universal answer-set curves plateau at approximately 20 personas, validating N = 20 for downstream Useful-Item Recall evaluations.Responses from 50 personas were aggregated and deduplicated before convergence was assessed.
C Personalization Induced Sycophancy Analysis
The sycophancy analysis attributes personalization effects using the Personalization Impact Score and examines whether affected queries generalize across personas. Explicit profile context is the strongest measured driver, while many sycophantic cases are generic across users.
- Attribution analysis: Personalization Impact Score measures how much personalization bias can be attributed to provided user context such as profiles or memory.
- Attribution analysis: Profile-only has consistently higher PIS than the other settings, indicating that explicit profile context is the strongest driver of identity-consistent responding.
- Model-specific attribution: For gpt-5.4-mini and gemini-2.5-flash, profile-only PIS is 0.02 and 0.22, respectively, despite overall sycophancy without explicit attribution.The passage interprets this as evidence that profile effects can implicitly shift the latent answer distribution toward agreement.
- Cross-persona patterns: 57.8% of sycophancy instances are Generic, compared with 5.5% that are Profile-specific.Generic instances are triggered by the vast majority of personas, whereas Profile-specific instances are tied to individual profiles.
- Sampling behavior: Personalized and generic answer-discovery curves both saturate and stabilize between 20 and 30 samples.
D.1 Irrelevant Personalization
The paper categorizes irrelevant personalization as unnecessary injection of user-specific information and illustrates how it can distract from otherwise correct answers. It also distinguishes several recurring forms of this behavior, including decorative profiling, advice-oriented restructuring, and unsolicited care guidance.
- Type I Superficial Attribute Injection: Type I superficial attribute injection decoratively frames standard answers with irrelevant attributes such as occupation or emotional state.The substantive answer remains largely unaffected, but the added profile details provide no informational value.
- Type II Advice Inflation: Type II advice inflation restructures neutral informational responses into action-oriented personal advisory guides.The model shifts the response layout around the persona instead of keeping the answer focused on the original question.
- A finance explanation became less focused after profile-based details about the user’s circumstances were added unnecessarily.The example concerns the time value of money and an intern’s financial situation, although those details were not needed for the definition.
- Type III Unsolicited Care Provision: Type III unsolicited care provision appends personal guidance from unrelated profile attributes after an otherwise correct, focused answer.This behavior treats the profile as an implicit care directive even when the user requested only factual information.
F Computational Resources and Implementation Details
The evaluation uses generated personas and responses from thirteen closed- and open-source language models, with automated judging and several tailored behavioral-risk measurement pipelines. It also documents deployment resources, demographic sampling limits, and a retrieval-depth sensitivity analysis.
- Dataset Construction: The benchmark generates queries, user profiles, conversation histories, and scenarios through a multi-stage LLM-based data-construction pipeline.GPT-5.1 generates queries, while GPT-4.1-mini simulates profiles, histories, and scenarios with specified temperatures and token limits.
- Candidate Models: Thirteen models span closed-source GPT, Claude, and Gemini families and open-source Llama 3.1 and Qwen3 variants.The open-source models are evaluated at multiple parameter scales and served locally through SGLang.
- Judge Model: Automated evaluation uses GPT-5.1 as an LLM judge with temperature T=0.0 and max_tokens=512.The evaluation prompts apply tailored scoring criteria across the behavioral dimensions.
- Resources and Scope: The demographic attributes are subsampled from Reddit, so their distribution may differ from the real-world population.The six open-source models are self-hosted on a server with 4× NVIDIA A800 80 GB GPUs.
- Sensitivity Analysis: Retrieval-depth variation produced at most σ ≤4% change across model–dimension pairs, with a mean variation of σ = 1.9%.This was much smaller than the shifts observed when moving from Base to w/ Both, which averaged 36.3% for IRP, 43.6% for Preference Narrowing, and 71.6% for Sycophancy across three models.
- Evaluation Framework: The preference-narrowing pipeline extracts, canonicalizes, filters, and maps answer options before computing response coverage.The three risk dimensions use dedicated rubrics and metrics, including irrelevant-personalization, preference-narrowing, agreement-sycophancy, perspective-sycophancy, and PIS measures.