Challenge: Large Language Models (LLMs) have led to significant improvements in various service domains, including search, recommendation, and chatbot applications.
Approach: They propose a framework for developing scalable, controllable, and reliable AI-driven agents that can be applied to real-world applications.
Outcome: The proposed framework bridges the gap between academic research and real-world application, and enables scalable, controllable, and reliable AI-driven agents.

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Towards large language model-based personal agents in the enterprise: Current trends and open problems (2023.findings-emnlp)

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Challenge: Existing large language models (LLMs) are brittle to input changes and can produce inconsistent results for the same inputs.
Approach: They propose to use large language models to reason about complex goals and orchestrate a set of pluggable tools or APIs to accomplish a goal.
Outcome: The proposed use cases have many open problems in an exciting area of NLP research, such as trust and explainability, consistency and reproducibility, and the need for new metrics and benchmarks.
Spoken Conversational Agents with Large Language Models (2025.emnlp-tutorials)

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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 .
Evaluating Conversational Agents with Persona-driven User Simulations based on Large Language Models: A Sales Bot Case Study (2025.emnlp-industry)

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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.
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When Speed Meets Intelligence: Scalable Conversational NER in an Ever-evolving World (2026.eacl-industry)

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Challenge: Large Language Models excel at understanding conversational semantics, but lack of data makes them impractical for production deployment.
Approach: They propose a pipeline for generating multilingual conversational NER datasets with minimal human validation and a framework that leverages LLMs as semantic filters combined with catalog-based entity grounding to label live traffic data.
Outcome: The proposed framework outperforms existing models on public and private conversations by 97.12% on CoNLL-2003 and 83.09% on OntoNotes 5.0.
ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models (2023.emnlp-demo)

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Challenge: Large language models (LLMs) have demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior.
Approach: They propose a framework that equips large language models with tool-use capabilities . they propose LLaMA and Chat-GLM as controllers, and a model-based agent framework .
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Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications (2026.acl-tutorials)

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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 .
ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents (2025.acl-long)

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Challenge: Existing models that use Large Language Models (LLMs) show superior performance in various tasks, but lack of controllability leads to unfocused conversations or task failure.
Approach: They propose a standard operating procedure (SOP) framework to regulate dialogue flow by integrating Chain of Thought reasoning and supervised fine-tuning for SOP prediction.
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Characteristic AI Agents via Large Language Models (2024.lrec-main)

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Challenge: Commercial products have been devoted to creating character-driven chatbots using large language models, but academic research in this area remains relatively scarce.
Approach: They investigate the performance of LLMs in constructing characteristic AI agents by simulating real-life individuals across different settings.
Outcome: The proposed benchmark compared LLMs with real-life individuals in different settings and includes evaluation metrics.
Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances, Resources, and Future Directions (2025.findings-emnlp)

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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 .
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Controllable and Reliable Knowledge-Intensive Task-Oriented Conversational Agents with Declarative Genie Worksheets (2025.acl-long)

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Challenge: Existing LLMs suffer from hallucination, following instructions with conditional logic, and integrating knowledge from different sources.
Approach: They propose a programmable framework for creating knowledge-intensive task-oriented conversational agents that handle involved interactions and answer complex queries.
Outcome: The proposed framework outperforms SOTA methods on complex logic dialogue datasets by up to 20.5%.

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