Battling against Tough Resister: Strategy Planning with Adversarial Game for Non-collaborative Dialogues (2025.acl-long)
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| Challenge: | Non-collaborative dialogue involves two participants with conflicting interests engaging in multiround dialogue to achieve their own goals. |
| Approach: | They propose a Game-based Adversarial self-play InterActive training paradigm which constructs an adversarial two-player (a persuader and a resister) zero-sum game and guides the game to approximate Nash Equilibrium (NE) via reinforcement learning. |
| Outcome: | The proposed model achieves state-of-the-art performance on three datasets. |
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| Challenge: | Recent research has demonstrated that goal-oriented dialogue agents can achieve striking performance when interacting with human users. |
| Approach: | They develop algorithms to evaluate the robustness of a goal-oriented dialogue agent by carefully designed attacks using adversarial agents. |
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Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation (2024.emnlp-main)
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| Challenge: | Non-collaborative dialogue agents are expected to engage in strategic conversations with diverse users, and this poses two main challenges for existing dialogue agents: 1) the inability to integrate user-specific characteristics into the strategic planning; 2) the difficulty of training strategic planners that can be generalized to diverse users. |
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ASTRO: Automatic Strategy Optimization For Non-Cooperative Dialogues (2025.findings-acl)
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| Challenge: | Existing methods for optimizing dialogues require substantial human effort for strategy optimization. |
| Approach: | They propose a fully automated solution that leverages large language models’ self-envolving capabilities to optimize dialogue strategies. |
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Teaching Models to Balance Resisting and Accepting Persuasion (2025.naacl-long)
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| Challenge: | Large language models (LLMs) are susceptible to persuasion, which can pose risks when faced with an adversarial interlocutor. |
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Toward Optimal LLM Alignments Using Two-Player Games (2025.findings-emnlp)
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Rui Zheng, Hongyi Guo, Zhihan Liu, Xiaoying Zhang, Yuanshun Yao, Xiaojun Xu, Zhaoran Wang, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang, Yang Liu, Hang Li
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ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations (2021.eacl-main)
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Ritam Dutt, Sayan Sinha, Rishabh Joshi, Surya Shekhar Chakraborty, Meredith Riggs, Xinru Yan, Haogang Bao, Carolyn Rose
| Challenge: | Existing research has failed to account for resisting strategies employed to foil persuasion attempts. |
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AGD: Adversarial Game Defense Against Jailbreak Attacks in Large Language Models (2025.acl-long)
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Shilong Pan, Zhiliang Tian, Zhen Huang, Wanlong Yu, Zhihua Wen, Xinwang Liu, Kai Lu, Minlie Huang, Dongsheng Li
| Challenge: | Existing defenses, including post-training alignment and prompt engineering, struggle with adaptability to out-of-distribution (OOD) attacks. |
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Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems (2020.findings-emnlp)
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| Challenge: | Dialogue policy learning for task-oriented dialogue systems has enjoyed great progress through using reinforcement learning methods. |
| Approach: | They propose a dialogue action decoder and a simulator-free adversarial learning method to improve dialogue agent performance without using reinforcement learning. |
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One Planner To Guide Them All ! Learning Adaptive Conversational Planners for Goal-oriented Dialogues (2025.emnlp-main)
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| Challenge: | Existing methods for goal-oriented dialogues involve training separate models for specific combinations of objectives, leading to computational and scalability issues. |
| Approach: | They propose a new dialogue policy method that can adapt to varying objective preferences at inference time without retraining. |
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Better LLM Reasoning via Dual-Play (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have made remarkable progress through Reinforcement Learning with Verifiable Rewards (RLVR) however, external supervision remains a bottleneck for tasks and domains for which supervised data are scarce or non-existent. |
| Approach: | They propose a novel dual-play framework that adversarially trains two models initialized from the same base model. |
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