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.
Contextualized Counterspeech Can Be More Persuasive Than Generic Counterspeech
arXiv:2607. 26236v1 Announce Type: cross Abstract: AI-generated counterspeech offers a scalable and effective strategy to mitigate online toxicity by promoting more constructive dialogue.
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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 AI
Jul 1
How Human Feedback Shapes AI-generated Community Notes
arXiv:2606. 30905v1 Announce Type: cross Abstract: Community Notes, a bridging-based crowd-sourced fact-checking system, has emerged as a new mechanism for moderating misleading information on social media and has been adopted by major platforms including X, Facebook, Instagram, Threads, and TikTok.
arXiv AI
Jul 7
Mecha-nudges for Machines
arXiv:2603. 23433v3 Announce Type: replace Abstract: AI agents are becoming active decision-makers on the Internet.
arXiv AI
Jun 9
"I understand your perspective": LLM Persuasion and Sycophancy through the Lens of Communicative Action Theory
arXiv:2606. 08076v1 Announce Type: cross Abstract: Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains largely unexplored.
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 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
Jul 22
"I understand your perspective": LLM Persuasion through the Lens of Communicative Action Theory
arXiv:2606. 08076v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains largely unexplored.
arXiv AI
Jul 2
Prompt Optimization for User Simulation in Conversational Recommender Systems: A Multi-Objective Framework
arXiv:2607. 00010v1 Announce Type: cross Abstract: Conversational recommender systems (CRSs) are a core component of next-generation intelligent recommender systems because they enable users to actively elicit preferences, clarify intentions, and adapt recommendations in real time.
arXiv AI
Jun 9
Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs
arXiv:2606. 09038v1 Announce Type: new Abstract: Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term histories.
arXiv AI
Jul 28
Steerable Chatbots: Exploring Personalization Control Interfaces via LLM Activation Steering
arXiv:2505. 04260v3 Announce Type: replace-cross Abstract: Personalizing LLM responses typically requires users to articulate their preferences through prompting, which can be burdensome at cold start and difficult to articulate in natural language.
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.