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. |
Similar Papers
Large Language Model-based Human-Agent Collaboration for Complex Task Solving (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in large language models have led to the development of LLM-based autonomous agents. |
| Approach: | They propose a Reinforcement Learning-based Human-Agent Collaboration method which trains a policy model to determine the most opportune stages for human intervention within the task-solving process. |
| Outcome: | The proposed method improves human-agent collaboration significantly through well-planned, limited human intervention. |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
Copied to clipboard
Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances, Resources, and Future Directions (2025.findings-emnlp)
Copied to clipboard
Yaozu Wu, Dongyuan Li, Yankai Chen, Renhe Jiang, Henry Peng Zou, Wei-Chieh Huang, Yangning Li, Liancheng Fang, Zhen Wang, Philip S. Yu
| Challenge: | Large Language Models (LLMs) are used to assist with driving decisions, but they face limitations in perception and computational demands. |
| Approach: | They propose a survey of LLM-based multi-agent ADSs and their applications . they analyze agent-human interactions in scenarios where LLM agents engage with humans . |
| Outcome: | The proposed approach reduces human intervention and improves safety and efficiency. |
Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues (2024.findings-emnlp)
Copied to clipboard
| 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. |
| Outcome: | The proposed model outperforms GPT-4 in many negotiation tasks while identifying specific challenges, such as making subjective assessments and generating contextually appropriate, strategically advantageous responses. |
Agent-to-Agent Theory of Mind: Testing Interlocutor Awareness among Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Prior work has focused on situational awareness, which refers to a model's ability to recognize its operating phase and constraints, but it has neglected the complementary capacity to identify and adapt to the identity and characteristics of a dialogue partner. |
| Approach: | They formalize interlocutor awareness and evaluate its emergence in contemporary LLMs. |
| Outcome: | The proposed model reliably identify same-family peers and certain prominent model families, such as GPT and Claude. |
Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications (2026.acl-tutorials)
Copied to clipboard
| Challenge: | Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems . |
| Approach: | This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems . it focuses on three core components: model distillation, dynamic routing, memory- and compute efficient serving . |
| Outcome: | This tutorial introduces state-of-the-art techniques for building efficient and efficient multi-agent LLM systems . it covers coordination and communication among agents, crucial for collective performance . |
Evaluating Conversational Agents with Persona-driven User Simulations based on Large Language Models: A Sales Bot Case Study (2025.emnlp-industry)
Copied to clipboard
Justyna Gromada, Alicja Kasicka, Ewa Komkowska, Lukasz Krajewski, Natalia Krawczyk, Morgan Veyret, Bartosz Przybył, Lina M. Rojas-Barahona, Michał K. Szczerbak
| Challenge: | Recent advances in LLMs enable sophisticated user simulations that can replace traditional rule-based evaluations. |
| Approach: | They propose a persona-driven approach to conversational agent evaluation using Large Language Models (LLMs) they introduce a dataset of customer personas, which are then used to configure a single LLM-based user simulator. |
| Outcome: | The proposed model emulates nuanced customer roles and can implement cross-selling strategies with minimal impact on customer satisfaction, varying by customer type. |
Hello Again! LLM-powered Personalized Agent for Long-term Dialogue (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing dialogue systems focus on brief single-session interactions, neglecting real-world needs for long-term companionship and personalized interactions. |
| Approach: | They propose a model-agnostic framework for long-term dialogue agents . they use event summary and persona management to enable reasoning . |
| Outcome: | The proposed framework incorporates three independently tunable modules dedicated to event perception, persona extraction, and response generation. |
Spoken Conversational Agents with Large Language Models (2025.emnlp-tutorials)
Copied to clipboard
| Challenge: | This tutorial focuses on the evolution of voice-native LLMs . it reviews the adaptation of text LLM to audio, cross-modal alignment, and joint speech–text training . |
| Approach: | This tutorial examines the evolution of voice-native LLMs in conversational agents . it compares cascaded and voice-based LLM systems to end-to-end retrieval-and vision-grounded systems . |
| Outcome: | This tutorial examines the evolution of voice-native LLMs . it compares the performance of voice assistants to current open-domain agents . |
LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)
Copied to clipboard
Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu
| Challenge: | Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. |
| Approach: | They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety. |
| Outcome: | The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system. |