arXiv Machine Learning
Jun 3
$\Psi$-Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues
arXiv:2606. 02754v1 Announce Type: new Abstract: Personalization is a crucial capability of modern language agents.
Benchmarking the Personalization Capabilities of Large Language Models
arXiv:2607. 20471v1 Announce Type: new Abstract: Personalization, the act of varying a message to induce action from a specific receiver while keeping sender, channel, and time fixed, has a long tradition in psychology and marketing as a two-party problem in which sender and receiver have independent objectives.
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arXiv Machine Learning
Jul 31
Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization
arXiv:2604. 07343v2 Announce Type: replace-cross Abstract: Pluralistic alignment has emerged as a critical frontier in the development of Large Language Models (LLMs), with reward models (RMs) serving as a central mechanism for capturing diverse human values.
arXiv AI
Jun 8
Re-Centering Humans in LLM Personalization
arXiv:2606. 06614v1 Announce Type: cross Abstract: Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data.
arXiv AI
Jun 6
A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing
arXiv:2606. 05330v1 Announce Type: cross Abstract: Large language models can shift human beliefs across high-stakes domains, but most persuasion studies rely on pre/post belief change.
arXiv Machine Learning
Jun 26
GRAG: Generic Response-Augmented Generation Framework for Personalized Conversational Systems
arXiv:2606. 21097v2 Announce Type: replace-cross Abstract: Deploying highly capable personalized conversational agents in resource-constrained or privacy-sensitive environments remains a significant challenge.
arXiv Machine Learning
Jun 30
Synthetic Interaction Data for Scalable Personalization in Large Language Models
arXiv:2602. 12394v2 Announce Type: replace Abstract: Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization methods primarily focus on task-level optimization while largely overlooking user-specific preferences and latent constraints of individual users.
arXiv AI
Jun 11
GrowLoop: Self-Evolving Conversation Evaluation Seeded by Human
arXiv:2605. 28882v2 Announce Type: replace-cross Abstract: With the rapid advancement of large language models, evaluating human-likeness in open-ended conversation has become increasingly important.
arXiv AI
1d ago
QUMem: Personalized Memory for Query-Conditioned User-State Inference in LLM Agents
arXiv:2608. 16168v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly use external memory systems to support personalization by drawing on long and evolving interaction histories, in which user preferences may be distributed across time, change with context, and conflict with earlier evidence.
arXiv AI
Jun 3
LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems
arXiv:2606. 02883v1 Announce Type: cross Abstract: Recommender systems have grown from content-organization tools into sophisticated systems that shape daily behavior.
arXiv AI
Aug 6
FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents
arXiv:2608. 04095v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used as personalized assistants in high-stakes domains such as financial advising, yet it remains unclear whether they can maintain and update an individualized user model over long horizons.
arXiv AI
Jul 24
Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents
arXiv:2602. 10226v2 Announce Type: replace-cross Abstract: Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hyperparameter search space and, more critically, designing sophisticated optimizers, architectures, and reward functions to capture nuanced user behaviors.
arXiv AI
Aug 3
Beyond Borrowed Histories: Person-Aligned User Simulation for Interactive Role-Playing Evaluation
arXiv:2607. 27816v2 Announce Type: replace-cross Abstract: Role-playing agents (RPAs) have become one of the most important consumer applications of large language models.