A Fairness-Driven Method for Learning Human-Compatible Negotiation Strategies (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in AI and NLP have led researchers to develop techniques to build autonomous agents which can achieve human-level performance in bargaining games such as Deal-orno-Deal. |
| Approach: | They propose a negotiation framework which incorporates fairness into reward design and search to learn human-compatible negotiation strategies. |
| Outcome: | The proposed framework achieves more egalitarian negotiation outcomes and improves negotiation quality. |
Similar Papers
A Game-Theoretica Negotiation Framework for Cross-Cultural Consensus (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models exhibit pronounced WEIRD cultural bias, marginalizing diverse viewpoints and posing challenges for reconciling diverse populations with varying cultural backgrounds and value systems. |
| Approach: | They propose a framework for cross-cultural fairness using a Nash Equilibrium . they propose equilibriums that iteratively propose and refine natural-language guidelines . |
| Outcome: | The proposed framework generates higher-quality and more balanced consensus . it finetunes diverse LLM architectures with negotiation data, reducing cultural distances by 95.53%. |
Optimizing Language Models with Fair and Stable Reward Composition in Reinforcement Learning (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent research has developed algorithms for reinforcement learning from human feedback and AI-generated feedback. |
| Approach: | They propose a method for reinforcement learning from human feedback and AI-generated feedback that incorporates weighting, ranking, and constraining to handle disparate rewards. |
| Outcome: | The proposed method reduces disparity and enhances stability among rewards . empirical results show that the proposed method is efficient and straightforward . |
Decoupling Strategy and Generation in Negotiation Dialogues (D18-1)
Copied to clipboard
| Challenge: | Recent work on negotiation trains neural models, but their end-to-end nature makes it hard to control their strategy. |
| Approach: | They propose a modular approach that decouples strategy and generation by coarse dialogue acts . they test their approach on a recently proposed DEALORNODEAL game . |
| Outcome: | The proposed approach can decouple strategy and generation without degeneracy. |
Let’s Negotiate! A Survey of Negotiation Dialogue Systems (2024.findings-eacl)
Copied to clipboard
Haolan Zhan, Yufei Wang, Zhuang Li, Tao Feng, Yuncheng Hua, Suraj Sharma, Lizhen Qu, Zhaleh Semnani Azad, Ingrid Zukerman, Reza Haf
| Challenge: | Recent research has focused on negotiation dialogue systems, but no systematic review of this task has been conducted. |
| Approach: | They propose to provide a systematic review of negotiation dialogue systems and to provide an overview of current research. |
| Outcome: | The proposed systems are based on the literature and are compared against existing systems. |
CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing systems that negotiate with humans have broad applications in pedagogy and conversational AI. |
| Approach: | They propose to annotate persuasion strategies and perform correlation analysis to understand how dialogue behaviors are associated with the negotiation performance. |
| Outcome: | The proposed system improves negotiation performance for all strategies labeled as skewed . the proposed system is available on github.com/kushalchawla/ . |
Assistive Large Language Model Agents for Socially-Aware Negotiation Dialogues (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have shown that virtual agents can help humans achieve task and social goals. |
| Approach: | They propose a tuning-free and label-free method to identify high-quality ICL exemplars for the remediator agent and propose measurable criteria to measure the quality of the negotiation outcomes. |
| Outcome: | The proposed model is able to improve negotiation outcomes across three negotiation topics. |
INA: An Integrative Approach for Enhancing Negotiation Strategies with Reward-Based Dialogue Agent (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a novel negotiation agent is designed for the online marketplace . a dialogue agent can negotiate on price and other factors . |
| Approach: | They propose a novel negotiation agent that is integrative in nature and can negotiate on price and other factors. |
| Outcome: | The proposed agent is integrative in nature and can negotiate on price and other factors. |
Should I Trust You? Detecting Deception in Negotiations using Counterfactual RL (2025.findings-acl)
Copied to clipboard
Wichayaporn Wongkamjan, Yanze Wang, Feng Gu, Denis Peskoff, Jonathan K. Kummerfeld, Jonathan May, Jordan Lee Boyd-Graber
| Challenge: | Future human-AI interaction tools can build on our methods for deception detection by triggering friction to give users a chance to interrogate suspicious proposals. |
| Approach: | They propose to use CTRL-D to detect deception in a board game called Diplomacy . CTRL is a counterfactual RL that has a good recall and almost perfect precision . future tools could build on this to reevaluate trust in suspicious negotiations . |
| Outcome: | The proposed method detects human deception with a high precision when compared to a Large Language Model approach that flags many true messages as deceptive. |
Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions (2023.emnlp-main)
Copied to clipboard
| Challenge: | A natural way to design a negotiation dialogue system is via self-play RL: train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data. |
| Approach: | They propose to use RL to train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data. |
| Outcome: | The proposed system fails to learn the value of compromise in a negotiation, which can lead to no agreements, and ultimately hurt the model's overall performance. |
CLHA: A Simple Yet Effective Contrastive Learning Framework for Human Alignment (2024.lrec-main)
Copied to clipboard
Feiteng Fang, Liang Zhu, Xi Feng, Jinchang Hou, Qixuan Zhao, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang
| Challenge: | Large language models (LLMs) have attracted considerable attention from academic and industrial communities due to their outstanding performance in various natural language processing tasks. |
| Approach: | They propose a Contrastive Learning Framework for Human Alignment to evaluate the noise within the data and dynamically adjust the training process. |
| Outcome: | The proposed framework surpasses other algorithms in terms of reward model scores, automatic evaluations, and human assessments on the widely used dataset "Helpful and Harmless" |