Exploring Safety-Utility Trade-Offs in Personalized Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Prior studies have shown that large language models can exhibit bias against specific demographic groups and engage in the generation of stereotypical responses. |
| Approach: | They propose a framework to evaluate LLM performance along two axes: safety and utility. |
| Outcome: | The proposed framework evaluates the performance of LLMs along two axes: safety and utility. |
Similar Papers
From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards (2024.findings-acl)
Copied to clipboard
Khaoula Chehbouni, Megha Roshan, Emmanuel Ma, Futian Wei, Afaf Taik, Jackie Cheung, Golnoosh Farnadi
| Challenge: | Recent advances in large language models have also introduced additional safety risks and raised concerns regarding their detrimental impact on already marginalized populations. |
| Approach: | They propose to use LLMs to evaluate their safety responses on already mitigated biases by evaluating models on already encoded assumptions. |
| Outcome: | The proposed model can encode harmful assumptions, but it can also be harmful for certain demographic groups. |
Benchmarking and Improving LLM Robustness for Personalized Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing evaluations focus on whether a model’s responses align with a user’s preferences, but factuality is an important yet overlooked dimension. |
| Approach: | They propose a scalable framework for evaluating robustness of large language models in personalization and a new dataset, PERGData. |
| Outcome: | The proposed framework improves robustness by 25% across models. |
Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation (2025.acl-long)
Copied to clipboard
Aneta Zugecova, Dominik Macko, Ivan Srba, Robert Moro, Jakub Kopál, Katarína Marcinčinová, Matúš Mesarčík
| Challenge: | Recent large language models generate disinformation news articles following predefined narratives . personalization and disinformation abilities of LLMs have not been studied . |
| Approach: | They evaluate the personalization and disinformation abilities of large language models . they find personalization reduces the safety-filter activations, thus effectively functioning as a jailbreak . |
| Outcome: | The proposed model generates disinformation news articles in english with the lowest quality of personalization. |
Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models (2025.findings-emnlp)
Copied to clipboard
Yeonjun In, Wonjoong Kim, Kanghoon Yoon, Sungchul Kim, Mehrab Tanjim, Sangwu Park, Kibum Kim, Chanyoung Park
| Challenge: | Extensive benchmarks evaluate LLM safety relying heavily on general standards . no benchmark datasets exist to evaluate the user-specific safety of LLMs . |
| Approach: | a new benchmark is designed to assess user-specific aspect of LLM safety . authors propose a simple remedy based on chain-of-thought to improve user-specified safety. |
| Outcome: | a new benchmark assesses the user-specific aspect of LLM safety . the proposed solution improves user-specified safety by chain-of-thought . |
ToolSpectrum: Towards Personalized Tool Utilization for Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches focus on functional tool selection following user instructions while overlooking the critical role of context-aware personalization in tool selection. |
| Approach: | They propose a benchmark to evaluate LLMs’ capabilities in personalized tool utilization. |
| Outcome: | The proposed benchmark evaluates LLMs' capabilities in personalized tool utilization. |
Can LLM be a Personalized Judge? (2024.findings-emnlp)
Copied to clipboard
| Challenge: | a new study examines the reliability of large language models (LLMs) for personalization and role-playing evaluation without examining its validity. |
| Approach: | They investigate the reliability of LLM-as-a-Personalized-Judge for personalization . they find that personas provided to LLMs have limited predictive power . |
| Outcome: | The proposed model is less reliable than previously thought, the authors show . human annotation reveals that third-person crowd worker evaluations of personalized preferences are even worse than LLM predictions. |
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models exhibit undesirable preference toward predicting certain answers over others, despite their adaptability to diverse tasks. |
| Approach: | They propose a label bias calibration method that outperforms recent calibration approaches for improving performance and mitigating label bias. |
| Outcome: | The proposed method outperforms calibration approaches for improving performance and mitigating label bias. |
Towards Understanding the Fragility of Multilingual LLMs against Fine-Tuning Attacks (2025.findings-naacl)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models have sparked concerns about their safety. |
| Approach: | They propose a method to identify safety-related information in the model parameter space . they propose to use a few adversarially chosen examples to fine-tune LLMs . |
| Outcome: | The proposed method can break safety alignment in multilingual LLMs using a few examples . it also shows that the proposed method jailbreaks LLM models adapted to new languages . |
Safety of Large Language Models Beyond English: A Systematic Literature Review of Risks, Biases, and Safeguards (2026.eacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have a growing number of applications that generate harmful, biased, or unsafe content. |
| Approach: | They synthesize findings from recent studies that evaluate their robustness across languages . they highlight gaps in multilingual safety research and recommend future work . |
| Outcome: | The systematic review examines the multilingual safety of large language models in English . it identifies challenges such as dataset availability and evaluation biases . |
User Behavior Prediction as a Generic, Robust, Scalable, and Low-Cost Evaluation Strategy for Estimating Generalization in LLMs (2025.findings-acl)
Copied to clipboard
| Challenge: | We argue that knowledge-retrieval and reasoning tasks are not ideal for measuring generalization, as LLMs are not trained for specific tasks. |
| Approach: | They propose a statistically motivated framework using personalization to assess generalization in Large Language Models. |
| Outcome: | The proposed framework outperforms existing models on movie and music recommendation datasets, but all models have room for improvement, especially Llama. |