arXiv Computation and Language

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

arXiv AI

Jul 3

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.

By Tianyi Zhang, Mousumi Das, Abrar Anwar, Jesse Thomason, David Traum

llms rag agents reinforcement-learning

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

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

arXiv AI

Aug 21

G-CARL: Grounded Checklist-Aligned Reward Learning for Patient-Oriented Medical Report Interpretation

arXiv:2608. 20331v1 Announce Type: cross Abstract: Personalized interpretation of medical reports has emerged as an increasingly important need among patients.

By Shiao Xie, Siyu Chen, Jianwei Lv, Bo Yuan, Yujin Wang, Xiandong Li

reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

2d ago

Learning Personalized Prompts for Healthcare Guidance

arXiv:2412.15957v2 Announce Type: replace-cross Abstract: The rapid development of large language models (LLMs) has transformed many industries, including healthcare. In practice, hospitals and patie...

By Ruize Shi, Hong Huang, Wei Zhou, Kehan Yin, Kai Zhao, Yun Zhao

llms reinforcement-learning

arXiv AI

Aug 13

Preference Tree Optimization: Enhancing Goal-Oriented Dialogue with Look-Ahead Simulations

arXiv:2608. 12062v1 Announce Type: cross Abstract: Developing dialogue systems capable of engaging in multi-turn, goal-oriented conversations remains a significant challenge, especially in specialized domains with limited data.

By Lior Baruch, Moshe Butman, Kfir Bar, Doron Friedman

agents reinforcement-learning

Hugging Face Trending Papers

Aug 20

G-CARL: Grounded Checklist-Aligned Reward Learning for Patient-Oriented Medical Report Interpretation

Personalized interpretation of medical reports has emerged as an increasingly important need among patients. Addressing this need requires both evidence-grounded medical factuality and context-dependent patient communication, yet existing medical vision-language tasks do not adequately capture these dual requirements.

reinforcement-learning fine-tuning multimodal benchmarks

arXiv Computation and Language

2d ago

Make an Offer They Can't Refuse: Grounding Bayesian Persuasion in Real-World Dialogues without Pre-Commitment

arXiv:2510.13387v3 Announce Type: replace Abstract: Large language models (LLMs) still struggle with strategic persuasion, largely because existing approaches either neglect information asymmetry or...

By Buwei He, Yang Liu, Zhaowei Zhang, Zixia Jia, Yang Yu, Huijia Wu, Zhaofeng He, Zilong Zheng, Yipeng Kang

llms fine-tuning

arXiv AI

Jun 3

Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis

arXiv:2606. 03812v1 Announce Type: new Abstract: Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems, demand reliable hazard identification.

By Sanjay Das, Ran Elgedawy, Ethan Seefried, Ryan Burchfield, Tirthankar Ghosal

llms agents nlp safety

arXiv AI

Jun 9

Explaining Black-Box Language Models: Learning to Optimize Linguistically-Structured Word Subsets

arXiv:2606. 08497v1 Announce Type: new Abstract: As deep language models (DLMs) are increasingly deployed in high-stakes domains such as healthcare, understanding their decision rationale becomes paramount for ensuring trust, safety, and accountability.

By Minyoung Hwang, Seokhyun Lee, Changhee Lee

llms benchmarks safety

arXiv Computation and Language

2d ago

Evaluating the Capabilities of LLMs for Persuasive Dialogue

The paper introduces “Persuasio”, a multi‑agent dialogue platform that uses a formal argumentation theory to adjudicate winners in free‑text debates. Using this system, the authors generated 192 debates on a UK political topic involving humans and large language models (LLMs), and evaluated 22 interlocutors through automated adjudication and 9,702 crowdsourced pairwise judgments across 1,386 annotation instances. The results show a consistent decoupling between subjective persuasiveness—where LLMs dominate—and formal argumentative strength—where humans remain competitive, with multi‑agent and retrieval‑augmented variants widening this gap.

By Jordan Robinson, Angus R. Williams, Katie Atkinson, Anthony G. Cohn

llms rag agents

Hugging Face Trending Papers

Jun 8

One Model, Multiple Goals: Adaptive Multi-Objective Learning for E-commerce Dialogue Systems

Dialogue systems in e-commerce scenarios often need to satisfy multiple objectives: accurately reasoning over user profiles (e. g.

agents reinforcement-learning benchmarks

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