# Benchmarking and Improving LLM Robustness for Personalized Generation

arXiv: https://arxiv.org/abs/2509.19358 · HTML: https://mlsanity.com/paper/2509.19358/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 56 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 30 of 30)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [Large Language Models Cannot Self-Correct Reasoning Yet](https://mlsanity.com/paper/2310.01798/) (2023)
- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://mlsanity.com/paper/2406.20094/) (2024)
- [State of What Art? A Call for Multi-Prompt LLM Evaluation](https://mlsanity.com/paper/2401.00595/) (2023)
- [To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning](https://mlsanity.com/paper/2409.12183/) (2024)
- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs](https://mlsanity.com/paper/2502.09597/) (2025)
- [Aligning Language Models to User Opinions](https://mlsanity.com/paper/2305.14929/) (2023)
- [Aligner: Efficient Alignment by Learning to Correct](https://mlsanity.com/paper/2402.02416/) (2024)
- [Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models](https://mlsanity.com/paper/2408.02442/) (2024)
- [How to Train Data-Efficient LLMs](https://mlsanity.com/paper/2402.09668/) (2024)
- [Aligning to Thousands of Preferences via System Message Generalization](https://mlsanity.com/paper/2405.17977/) (2024)
- [Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting](https://mlsanity.com/paper/2309.07034/) (2023)
- [Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering](https://mlsanity.com/paper/1911.07176/) (2019)
- [Robustness of Learning from Task Instructions](https://mlsanity.com/paper/2212.03813/) (2022)
- [Exploring the Robustness of Decision-Level Through Adversarial Attacks on LLM-Based Embodied Models](https://mlsanity.com/paper/2405.19802/) (2024)
- [Learning Personalized Alignment for Evaluating Open-ended Text Generation](https://mlsanity.com/paper/2310.03304/) (2023)
- [Improving Context-Aware Preference Modeling for Language Models](https://mlsanity.com/paper/2407.14916/) (2024)
- [Compositional Demographic Word Embeddings](https://mlsanity.com/paper/2010.02986/) (2020)
- [Refocusing on Relevance: Personalization in NLG](https://mlsanity.com/paper/2109.05140/) (2021)
- [Value Imprint: A Technique for Auditing the Human Values Embedded in RLHF Datasets](https://mlsanity.com/paper/2411.11937/) (2024)
- [Consistent Aggregation of Objectives with Diverse Time Preferences Requires Non-Markovian Rewards](https://mlsanity.com/paper/2310.00435/) (2023)
- [Data-efficient LLM Fine-tuning for Code Generation](https://mlsanity.com/paper/2504.12687/) (2025)
- [Fast Proxies for LLM Robustness Evaluation](https://mlsanity.com/paper/2502.10487/) (2025)

## Built on by (most cited first, 4 of 4)

- [One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization](https://mlsanity.com/paper/2601.18572/) (2026)
- [When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs](https://mlsanity.com/paper/2601.11000/) (2026)
- [Toward Robust Personalized Alignment for LLMs: Mitigating Persona Drift in Multi-Turn Dialogue](https://mlsanity.com/paper/2609.12373/) (2026)
- [Can Large Language Models Make Everyone Happy?](https://mlsanity.com/paper/2602.11091/) (2026)

## Abstract (arXiv)

Recent years have witnessed a growing interest in personalizing the responses of large language models (LLMs). While existing evaluations primarily focus on whether a response aligns with a user's preferences, we argue that factuality is an equally important yet often overlooked dimension. In the context of personalization, we define a model as robust if its responses are both factually accurate and align with the user preferences. To assess this, we introduce PERG, a scalable framework for evaluating robustness in LLMs, along with a new dataset, PERGData. We evaluate fourteen models from five different model families using different prompting methods. Our findings show that current LLMs struggle with robust personalization: even the strongest models (GPT-4.1, LLaMA3-70B) fail to maintain correctness in 5% of previously successful cases without personalization, while smaller models (e.g., 7B-scale) can fail more than 20% of the time. Further analysis reveals that robustness is significantly affected by the nature of the query and the type of user preference. To mitigate these failures, we propose Pref-Aligner, a two-stage approach that improves robustness by an average of 25% across models. Our work highlights critical gaps in current evaluation practices and introduces tools and metrics to support more reliable, user-aligned LLM deployments.