| 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 . |
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| Challenge: | Recent advances in large language models have spurred interest in expanding their application beyond text-based tasks. |
| Approach: | They propose to categorize the integration of speech with LLMs into three main approaches . they demonstrate how these methods are applied across various speech-related applications . |
| Outcome: | The proposed methods are applied across speech-related applications and highlight the challenges in this field to offer inspiration for future research. |
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. |
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A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs (2025.acl-industry)
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Chiwan Park, Wonjun Jang, Daeryong Kim, Aelim Ahn, Kichang Yang, Woosung Hwang, Jihyeon Roh, Hyerin Park, Hyosun Wang, Min Seok Kim, Jihoon Kang
| Challenge: | Large Language Models (LLMs) have led to significant improvements in various service domains, including search, recommendation, and chatbot applications. |
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Hello Again! LLM-powered Personalized Agent for Long-term Dialogue (2025.naacl-long)
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| Challenge: | Existing dialogue systems focus on brief single-session interactions, neglecting real-world needs for long-term companionship and personalized interactions. |
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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 . |
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LLMs syntactically adapt their language use to their conversational partner (2025.acl-short)
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| Challenge: | Adapting to the language of a communication partner is associated with increased success in goal-oriented conversations. |
| Approach: | They construct a corpus of conversations between large language models (LLMs) and measure their syntactic adaptation. |
| Outcome: | The proposed model can adapt to the language of the conversational partner in at least a rudimentary way. |
From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models (2026.findings-acl)
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| Challenge: | Existing definitions of streaming LLMs are fragmented and lack a systematic taxonomy . large language models are pre-trained on static and full-context corpora . |
| Approach: | They propose a systematic taxonomy of current streaming Large Language Models and propose underlying methodologies for streaming LLMs. |
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It’s Not under the Lamppost: Expanding the Reach of Conversational AI (2024.lrec-main)
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| Challenge: | Focused probes into the capabilities of language-based assistants easily reveal significant areas of brittleness that demonstrate large gaps in their coverage. |
| Approach: | They propose a process for collecting specific kinds of data to uncover these gaps and an annotation scheme for system responses. |
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LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models (2024.findings-acl)
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| Challenge: | ***LLaST*** is a framework for building high-performance Large Language model based Speech-to-text Translation systems. |
| Approach: | They propose a framework for building high-performance Large Language model based Speech-to-text Translation systems. |
| Outcome: | The proposed model outperforms the CoVoST-2 benchmark and showcases exceptional scaling capabilities powered by LLMs. |
Deep Learning for Conversational AI (N18-6)
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| Challenge: | Spoken Dialogue Systems (SDS) have great commercial potential . the advent of deep learning has led to significant advances in this area of NLP research . |
| Approach: | This tutorial will introduce researchers to the pipeline framework for modelling goal-oriented dialogue systems. |
| Outcome: | This tutorial will familiarise researchers with the latest advances in spoken dialogue systems . the aim of the course is to encourage dialogue research in the NLP community . |