{"total":84,"rows":[{"id":"2502.09597","title":"Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs","created":"2025-02-13","cited_by_count":148,"area":null},{"id":"2406.05761","title":"The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models","created":"2024-06-09","cited_by_count":93,"area":null},{"id":"2406.10471","title":"Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts","created":"2024-06-15","cited_by_count":74,"area":null},{"id":"2502.11528","title":"A Survey of Personalized Large Language Models: Progress and Future Directions","created":"2025-02-17","cited_by_count":71,"area":null},{"id":"2410.04070","title":"PAD: Personalized Alignment of LLMs at Decoding-Time","created":"2024-10-05","cited_by_count":60,"area":"alignment"},{"id":"2501.13381","title":"Do as We Do, Not as You Think: the Conformity of Large Language Models","created":"2025-01-23","cited_by_count":53,"area":"lr"},{"id":"2502.08745","title":"IHEval: Evaluating Language Models on Following the Instruction Hierarchy","created":"2025-02-12","cited_by_count":53,"area":null},{"id":"2410.16491","title":"BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded Data","created":"2024-10-21","cited_by_count":50,"area":null},{"id":"2503.17003","title":"A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications","created":"2025-03-21","cited_by_count":42,"area":"alignment"},{"id":"2410.03868","title":"Can Language Models Reason about Individualistic Human Values and Preferences?","created":"2024-10-04","cited_by_count":34,"area":"lr"},{"id":"2506.23235","title":"Generalist Reward Models: Found Inside Large Language Models","created":"2025-06-29","cited_by_count":34,"area":"alignment"},{"id":"2502.15851","title":"Control Illusion: The Failure of Instruction Hierarchies in Large Language Models","created":"2025-02-21","cited_by_count":33,"area":null},{"id":"2502.12197","title":"A Closer Look at System Prompt Robustness","created":"2025-02-15","cited_by_count":32,"area":"safety"},{"id":"2408.10943","title":"SysBench: Can Large Language Models Follow System Messages?","created":"2024-08-20","cited_by_count":28,"area":null},{"id":"2504.07070","title":"A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models","created":"2025-04-09","cited_by_count":27,"area":"alignment"},{"id":"2510.26202","title":"What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data","created":"2025-10-30","cited_by_count":26,"area":"alignment"},{"id":"2407.14916","title":"Improving Context-Aware Preference Modeling for Language Models","created":"2024-07-20","cited_by_count":22,"area":"alignment"},{"id":"2508.01674","title":"CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactions","created":"2025-08-03","cited_by_count":22,"area":null},{"id":"2509.10127","title":"Population-Aligned Persona Generation for LLM-based Social Simulation","created":"2025-09-12","cited_by_count":21,"area":null},{"id":"2406.11096","title":"The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models","created":"2024-06-16","cited_by_count":19,"area":null},{"id":"2408.16482","title":"Self-Alignment: Improving Alignment of Cultural Values in LLMs via In-Context Learning","created":"2024-08-29","cited_by_count":19,"area":"alignment"},{"id":"2501.11549","title":"Whose Boat Does it Float? Improving Personalization in Preference Tuning via Inferred User Personas","created":"2025-01-20","cited_by_count":19,"area":"alignment"},{"id":"2502.16320","title":"Direct Alignment with Heterogeneous Preferences","created":"2025-02-22","cited_by_count":19,"area":"alignment"},{"id":"2505.21091","title":"Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)","created":"2025-05-27","cited_by_count":19,"area":null},{"id":"2505.09666","title":"System Prompt Optimization with Meta-Learning","created":"2025-05-14","cited_by_count":18,"area":null},{"id":"2503.00771","title":"Evaluating Personalized Tool-Augmented LLMs from the Perspectives of Personalization and Proactivity","created":"2025-03-02","cited_by_count":17,"area":"agents"},{"id":"2505.18882","title":"Personalized Safety in LLMs: A Benchmark and A Planning-Based Agent Approach","created":"2025-05-24","cited_by_count":16,"area":"agents"},{"id":"2502.14289","title":"Drift: Decoding-time Personalized Alignments with Implicit User Preferences","created":"2025-02-20","cited_by_count":13,"area":"alignment"},{"id":"2509.25137","title":"The Era of Real-World Human Interaction: RL from User Conversations","created":"2025-09-29","cited_by_count":13,"area":null},{"id":"2504.12663","title":"Persona-judge: Personalized Alignment of Large Language Models via Token-level Self-judgment","created":"2025-04-17","cited_by_count":11,"area":"alignment"},{"id":"2505.24846","title":"MiCRo: Mixture Modeling and Context-aware Routing for Personalized Preference Learning","created":"2025-05-30","cited_by_count":11,"area":"alignment"},{"id":"2509.19333","title":"Pluralistic Off-policy Evaluation and Alignment","created":"2025-09-15","cited_by_count":11,"area":"alignment"},{"id":"2510.23451","title":"Omni-Reward: Towards Generalist Omni-Modal Reward Modeling with Free-Form Preferences","created":"2025-10-27","cited_by_count":11,"area":"alignment"},{"id":"2502.15086","title":"Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models","created":"2025-02-20","cited_by_count":10,"area":"safety"},{"id":"2505.23785","title":"Meaning Is Not A Metric: Using LLMs to make cultural context legible at scale","created":"2025-05-23","cited_by_count":10,"area":null},{"id":"2408.05094","title":"Unlocking Decoding-time Controllability: Gradient-Free Multi-Objective Alignment with Contrastive Prompts","created":"2024-08-09","cited_by_count":9,"area":"alignment"},{"id":"2407.03958","title":"Stark: Social Long-Term Multi-Modal Conversation with Persona Commonsense Knowledge","created":"2024-07-04","cited_by_count":8,"area":null},{"id":"2509.24696","title":"T-POP: Test-Time Personalization with Online Preference Feedback","created":"2025-09-29","cited_by_count":7,"area":"alignment"},{"id":"2410.14940","title":"Baichuan Alignment Technical Report","created":"2024-10-19","cited_by_count":6,"area":"alignment"},{"id":"2506.07165","title":"AMoPO: Adaptive Multi-objective Preference Optimization without Reward Models and Reference Models","created":"2025-06-08","cited_by_count":6,"area":"alignment"},{"id":"2510.17881","title":"POPI: Personalizing LLMs via Optimized Natural Language Preference Inference","created":"2025-10-17","cited_by_count":6,"area":null},{"id":"2505.18071","title":"Extended Inductive Reasoning for Personalized Preference Inference from Behavioral Signals","created":"2025-05-23","cited_by_count":5,"area":"lr"},{"id":"2510.18526","title":"Counterfactual Reasoning for Steerable Pluralistic Value Alignment of Large Language Models","created":"2025-10-21","cited_by_count":5,"area":"lr"},{"id":"2604.10925","title":"From Words to Widgets for Controllable LLM Generation","created":"2026-04-13","cited_by_count":5,"area":null},{"id":"2412.20834","title":"Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment","created":"2024-12-30","cited_by_count":4,"area":"alignment"},{"id":"2506.14200","title":"ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanations","created":"2025-06-17","cited_by_count":4,"area":null},{"id":"2509.19358","title":"Benchmarking and Improving LLM Robustness for Personalized Generation","created":"2025-09-18","cited_by_count":4,"area":null},{"id":"2511.02463","title":"Extending RLVR to Open-Ended Tasks via Verifiable Multiple-Choice Reformulation","created":"2025-11-04","cited_by_count":4,"area":"lr"},{"id":"2602.12116","title":"P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling","created":"2026-02-12","cited_by_count":4,"area":"alignment"},{"id":"2602.12394","title":"Synthetic Interaction Data for Scalable Personalization in Large Language Models","created":"2026-02-12","cited_by_count":4,"area":null},{"id":"2602.17695","title":"EXACT: Explicit Attribute-Guided Decoding-Time Personalization","created":"2026-02-06","cited_by_count":4,"area":null},{"id":"2604.12479","title":"Meet Dynamic Individual Preferences: Resolving Conflicting Human Value with Paired Fine-Tuning","created":"2026-04-14","cited_by_count":4,"area":"alignment"},{"id":"2411.04496","title":"Thanos: Enhancing Conversational Agents with Skill-of-Mind-Infused Large Language Model","created":"2024-11-07","cited_by_count":3,"area":"agents"},{"id":"2509.16543","title":"ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructions","created":"2025-09-20","cited_by_count":3,"area":"synth"},{"id":"2510.11952","title":"GRAVITY: A Framework for Personalized Text Generation via Profile-Grounded Synthetic Preferences","created":"2025-10-13","cited_by_count":3,"area":null},{"id":"2605.24647","title":"Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversation","created":"2026-05-23","cited_by_count":3,"area":null},{"id":"2502.11330","title":"System Message Generation for User Preferences using Open-Source Models","created":"2025-02-17","cited_by_count":2,"area":null},{"id":"2503.08035","title":"Group Preference Alignment: Customized LLM Response Generation from In-Situ Conversations","created":"2025-03-11","cited_by_count":2,"area":"alignment"},{"id":"2503.10669","title":"UC-MOA: Utility-Conditioned Multi-Objective Alignment for Distributional Pareto-Optimality","created":"2025-03-10","cited_by_count":2,"area":"alignment"},{"id":"2505.20336","title":"MOSLIM:Align with diverse preferences in prompts through reward classification","created":"2025-05-24","cited_by_count":2,"area":"alignment"},{"id":"2509.11311","title":"Prompts to Proxies: Emulating Human Preferences via a Compact LLM Ensemble","created":"2025-09-14","cited_by_count":2,"area":"alignment"},{"id":"2601.18730","title":"Reflect: Transparent Principle-Guided Reasoning for Constitutional Alignment at Scale","created":"2026-01-26","cited_by_count":2,"area":"lr"},{"id":"2410.04751","title":"Intriguing Properties of Large Language and Vision Models","created":"2024-10-07","cited_by_count":1,"area":null},{"id":"2410.16392","title":"Scaffolded Language Models with Language Supervision for Mixed-Autonomy: A Survey","created":"2024-10-21","cited_by_count":1,"area":null},{"id":"2410.21597","title":"Reducing the Scope of Language Models","created":"2024-10-28","cited_by_count":1,"area":null},{"id":"2411.01245","title":"PMoL: Parameter Efficient MoE for Preference Mixing of LLM Alignment","created":"2024-11-02","cited_by_count":1,"area":"alignment"},{"id":"2512.15793","title":"Explainable Ethical Assessment on Human Behaviors by Generating Conflicting Social Norms","created":"2025-12-16","cited_by_count":1,"area":null},{"id":"2604.17968","title":"From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives?","created":"2026-04-20","cited_by_count":1,"area":null},{"id":"2605.28073","title":"StoryLens: Preference-Aligned Story Rewriting via Context-Aware Narrative Enrichment","created":"2026-05-27","cited_by_count":1,"area":"alignment"},{"id":"2607.09175","title":"Scoped Verification for Reliable Long-Horizon Agentic Context Evolution under Distribution Shift","created":"2026-07-10","cited_by_count":1,"area":"agents"},{"id":"2607.17043","title":"Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning","created":"2026-07-19","cited_by_count":1,"area":"synth"},{"id":"2609.04738","title":"Aplaud: Adaptive Personalized Low-Rank Decomposition for User-Specific LLM","created":"2026-09-04","cited_by_count":1,"area":null},{"id":"2609.33763","title":"SecProbe: Adaptive Evaluation of Coding Agents on Cybersecurity Vulnerabilities","created":"2026-09-27","cited_by_count":1,"area":"agents"},{"id":"2406.10203","title":"A Probability--Quality Trade-off in Aligned Language Models and its Relation to Sampling Adaptors","created":"2024-06-14","cited_by_count":0,"area":"alignment"},{"id":"2506.11098","title":"Debiasing Online Preference Learning via Preference Feature Preservation","created":"2025-06-06","cited_by_count":0,"area":"alignment"},{"id":"2603.19294","title":"Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2605.01831","title":"RMGAP: Benchmarking the Generalization of Reward Models across Diverse Preferences","created":"2026-05-03","cited_by_count":0,"area":"alignment"},{"id":"2605.25342","title":"MATO: Multi-objective Personalized Alignment with Test-time Optimization for Large Language Models","created":"2026-05-25","cited_by_count":0,"area":"alignment"},{"id":"2606.30339","title":"REAR: Test-time Preference Realignment through Reward Decomposition","created":"2026-06-29","cited_by_count":0,"area":"alignment"},{"id":"2607.20485","title":"Expectation Alignment of Language Models for Real-World User Expectations","created":"2026-06-02","cited_by_count":0,"area":"alignment"},{"id":"2608.19207","title":"Compliance, Capability, and Conflict: Benchmarking Multimodal LLMs under System Messages","created":"2026-06-12","cited_by_count":0,"area":"mllm"},{"id":"2609.02265","title":"CAPTURE: Disentangling Preference Drift from Memory Poisoning in Personalized LLM Agents","created":"2026-09-02","cited_by_count":0,"area":"agents"},{"id":"2609.05899","title":"AlignDiff: Exploiting Model-Intrinsic Information for Better Preference Data Selection","created":"2026-09-05","cited_by_count":0,"area":"alignment"},{"id":"2609.26853","title":"COPE: Continual Personalization of LLMs under Sparse User Feedback via User Embeddings and Self-Evaluation","created":"2026-09-22","cited_by_count":0,"area":null}],"next_cursor":null}