arXiv AI

DiPS: Dialogue Policy Selection for High-Stakes Persuasion Agents

arXiv:2607. 01557v1 Announce Type: cross Abstract: Large Language Models (LLMs) often struggle with persuasion in high-stakes scenarios.

arXiv Computation and Language

Sep 2

PersuaRL: Reinforcement Learning-Driven Multi-Expert Selection for Persuasive Dialogue Generation in Insurance

arXiv:2609.01188v1 Announce Type: new Abstract: Large Language Models (LLMs) are revolutionizing digital communication by powering conversational agents deployed across domains such as customer servi...

By Rohan Kirti, Akash Ghosh, Aryan Vats, Niladri Ghosh, Shipra Shriparn, Roshni Ramnani, Anutosh Maitra, Sriparna Saha

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 15

UP-NRPA: User Portrait based Nested Rollout Policy Adaptation for Planning with Large Language Models in Goal-oriented Dialogue Systems

arXiv:2606. 13683v1 Announce Type: new Abstract: To address the challenge that current dialogue policy planning methods struggle to dynamically adapt to diverse user characteristics, this paper proposes a User Portrait based Nested Rollout Policy Adaptation (UP-NRPA) online framework with Large Language Models.

By Hui Wang, Fafa Zhang, Meng Liu, Xiangyu Chen, Chaoxu Mu

llms reinforcement-learning benchmarks

arXiv Computation and Language

Aug 25

ToSCA: Leveraging Hierarchical Reinforcement Learning on Temporal and Strategic Abstractions of Conversational Agents

arXiv:2608.21969v1 Announce Type: new Abstract: Humans have multiple levels of temporal abstractions on daily interaction and thinking, such as concept perception and strategic planning. Inspired by...

By Xiaoyu Wang, Qingqing Gu, Yue Zhao, Teng Chen, Yuqi Cao, Xiaokai Chen, Hongyan Li, Luo Ji

agents reinforcement-learning efficiency

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.

By Peixuan Han, Hongyi Du, Jiayu Liu, Yihang Sun, Yutong Liu, Jiaxuan You

llms agents benchmarks

arXiv AI

Jul 1

LLM-Driven Personalities for Decision Making in Emergency Simulations

arXiv:2606. 31038v1 Announce Type: cross Abstract: For virtual humans to appear believable, they must exhibit agency and spatial awareness while interacting with their environment in ways that reflect competence and intelligence.

By Stefano Calzolari, Rubens Montanha, Gabriel Schneider, Gustavo Wide, Paulo Knob, Francesco Strada, Andrea Bottino, Soraia Raupp Musse

llms agents

arXiv AI

Aug 13

Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs

arXiv:2608. 11624v1 Announce Type: cross Abstract: Persuasion is a core dynamic of natural language communication, shaping how large language models (LLMs) update beliefs, resolve disagreements, and reach decisions.

By Nimet Beyza Bozdag, Emre Can Acikgoz, Gokhan Tur, Dilek Hakkani-T\"ur

llms agents reinforcement-learning safety

arXiv Computation and Language

Sep 10

MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation

arXiv:2510.05124v3 Announce Type: replace Abstract: We propose MADS (Multi-Agent Dialogue Simulation), a scalable framework for generating persuasive multi-turn dialogues via agent self-play. MADS em...

By Mingjin Li, Yu Liu, Huayi Liu, Xiang Ye, Chao Jiang, Hongguang Zhang, Yu Ruan

llms agents reinforcement-learning

arXiv AI

Jul 24

Robust Critics: Defending LLMs Against Multi-Turn Attacks

arXiv:2607. 20472v1 Announce Type: new Abstract: When a user asks a language model something harmful, is it a genuine attack or a misunderstood but well-meaning question?

By Roman Belaire, Arunesh Sinha, Pradeep Varakantham

llms reinforcement-learning fine-tuning safety

arXiv AI

Aug 18

Ask to Be Sure: Informative Interactions for Confident Multi-Turn LLM Recommendation

arXiv:2608. 15949v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled their use as conversational recommender systems (CRS), demonstrating strong recommendation accuracy and natural dialogue.

By Cedar Site Bai, Duanshun Li, Zhenyu Liao, Sheikh Sarwar, Huiyuan Chen, Yuan Chen, Changhe Yuan, Haiyang Zhang, Qilin Qi

llms agents reinforcement-learning fine-tuning

Hugging Face Trending Papers

Aug 12

Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs

Persuasion is a core dynamic of natural language communication, shaping how large language models (LLMs) update beliefs, resolve disagreements, and reach decisions. As LLMs increasingly debate, advise, and think collaboratively with humans and each other, resistance to harmful persuasion becomes a core requirement for reliable behavior.

llms agents reinforcement-learning safety

arXiv Machine Learning

Aug 27

Large Language Model Few-Shot Prompting with Dilemma Training Outperforms Human Surrogates in Predicting Patient Preferences

The paper introduces P4-DT, a personalized patient preference predictor that uses dilemma training to elicit context‑dependent decision reasoning. In a study of 12 patient‑surrogate pairs, P4‑DT achieved 81.7% accuracy in predicting patient treatment choices, outperforming unassisted surrogates (55.0%) and surrogates aided by a simpler P4 model (61.7%). The authors show that incorporating contextual scenarios and open‑ended text into prompts improves accuracy by 15 percentage points over static value ratings.

By Natasha Ureyang, Sebastian Porsdam Mann, Yuxin Liu, Zuriel Hassirim, Melanie Almonte, Wenhao Chen, Joyce Ng, Thant Nay Lin, Aung Thiha, Gerald CH Koh, Brian David Earp, Pin Sym Foong

llms agents

arXiv AI

Jul 10

SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection

arXiv:2601. 02871v3 Announce Type: replace Abstract: Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-media contacts for private-channel conversion.

By Zhiyong Cao, Dunqiang Liu, Qi Dai, Haojun Xu, Huai Yuen Khor, Hao Wang, Huan He, Yafei Liu, Ke Ma, Ruqian Shi, Sicheng Zhou, Sijia Yao

agents reinforcement-learning fine-tuning

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