| Challenge: | The rapid progress of LLMs has led to the development of more sophisticated AI tutoring systems. |
| Approach: | They develop an LLM-based assistant for coaching negotiation that provides users with targeted feedback for improvement. |
| Outcome: | The proposed system improves negotiation performance significantly compared to a system that doesn’t provide feedback and one which uses an alternative method. |
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| Challenge: | Large Language Models (LLMs) are increasingly being utilized as AI negotiation agents . however, prior research on LLMs lacks a systematic evaluation of their diverse capabilities in negotiation. |
| Approach: | They propose to analyze the multifaceted capabilities of Large Language Models (LLMs) across diverse dialogue scenarios throughout the stages of a typical negotiation interaction. |
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MERIT Feedback Elicits Better Bargaining in LLM Negotiators (2026.acl-long)
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| Challenge: | Empirical results indicate that baseline LLM strategies diverge from human preferences, while our mechanism substantially improves negotiation performance. |
| Approach: | They propose a utility feedback centric framework that measures human-aligned, economically grounded metrics that implicitly measure how well the negotiation aligns with human preference. |
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Let’s Negotiate! A Survey of Negotiation Dialogue Systems (2024.findings-eacl)
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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. |
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Assistive Large Language Model Agents for Socially-Aware Negotiation Dialogues (2024.findings-emnlp)
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| 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. |
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| Challenge: | Using a novel approach, we can evaluate an agent’s bargaining abilities as an asymmetric incomplete information game. |
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LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement (2024.findings-emnlp)
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| Challenge: | Using advanced Large Language Models, instructors can improve training of smaller models by analyzing their own model's errors. |
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Agentic AI for Human Resources: LLM-Driven Candidate Assessment (2026.eacl-demo)
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Kamer Ali Yuksel, Abdul Basit Anees, Ashraf Hatim Elneima, Sanjika Hewavitharana, Mohamed Al-Badrashiny, Hassan Sawaf
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Assistant-Guided Mitigation of Teacher Preference Bias in LLM-as-a-Judge (2025.findings-emnlp)
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| Challenge: | LLM-as-a-Judge uses large language models to evaluate the quality of LLM generated responses, but training proxy judge models using evaluation data generated by powerful teacher models introduces a critical yet previously overlooked issue: teacher preference bias. |
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Preference Learning Unlocks LLMs’ Psycho-Counseling Skills (2026.findings-acl)
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| Challenge: | Current LLMs struggle to consistently provide effective responses to client speeches due to the lack of supervision from high-quality real psycho-counseling data. |
| Approach: | They propose to use a dataset to evaluate therapists' responses to client speeches using a set of professional and comprehensive principles to evaluate their responses. |
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Position: LLMs Can be Good Tutors in English Education (2025.emnlp-main)
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Jingheng Ye, Shen Wang, Deqing Zou, Yibo Yan, Kun Wang, Hai-Tao Zheng, Ruitong Liu, Zenglin Xu, Irwin King, Philip S. Yu, Qingsong Wen
| Challenge: | Recent efforts to integrate large language models into English education lack adaptability to language learning. |
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