Papers with natural language interaction
AgentMaster: A Multi-Agent Conversational Framework Using A2A and MCP Protocols for Multimodal Information Retrieval and Analysis (2025.emnlp-demos)
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| Challenge: | Recent advances in AI focus on multi-agent systems (MAS) that can be integrated with Large Language Models (LLMs) but current systems still face challenges of inter-agency communication, coordination, and interaction with heterogeneous tools and resources. |
| Approach: | They propose a modular multi-protocol MAS framework with self-implemented A2A and MCP . the framework supports natural language interaction without prior technical expertise . |
| Outcome: | The proposed framework supports natural language interaction without prior technical expertise and responds to multimodal queries for tasks including information retrieval, question answering, and image analysis. |
Honkling: In-Browser Personalization for Ubiquitous Keyword Spotting (D19-3)
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| Challenge: | keyword spotting systems are used for simple commands recognition on devices . however, voice-enabled web applications are few and far between . a prominent drawback is that most of these systems perform speech recognition in the cloud . |
| Approach: | Honkling is a JavaScript-based keyword spotting system that can be deployed on user devices. |
| Outcome: | Honkling is a JavaScript-based keyword spotting system that can be deployed on user devices. |
SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs (2026.acl-industry)
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Prateek Chaturvedi, Yuqicheng Zhu, Hongkuan Zhou, Dongzhuoran Zhou, Yunjie He, Steffen Staab, Fei Du, Jie Tang, Evgeny Kharlamov
| Challenge: | Existing agentic approaches for Knowledge Graph-based Retrieval-Augmented Generation fail to generalize to real-world enterprise Knowledge graphs (KGs) dense, schema-driven, and operationally constrained, requiring a training-free framework. |
| Approach: | They propose a training-free framework that integrates structured planning with controlled iterative reasoning by injecting schema-conditioned structural priors and enforcing schemas during multi-hop reasoning. |
| Outcome: | The proposed framework significantly improves on a real-world enterprise-oriented benchmark constructed from a Configuration Management DataBase (CMDB). |
GRV-KBQA: A Three-Stage Framework for Knowledge Base Question Answering with Decoupled Logical Structure, Semantic Grounding and Structure-Aware Validation (2025.findings-emnlp)
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| Challenge: | Existing methods for Knowledge Base Question Answering generate non-executable queries and inefficiencies in query execution. |
| Approach: | a framework that decouples logical structure generation from semantic grounding is proposed . the framework explicitly enforces KB constraints to improve alignment between generated logical forms and KB structures. |
| Outcome: | GRV-KBQA decouples logical structure generation from semantic grounding and incorporates structure-aware validation to enhance accuracy. |
Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback (2020.acl-main)
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| Challenge: | a natural language interface (NLI) can be used to correct semantic parsing errors . human correction accuracy is 81.5%, but the best model achieves only 25.1% . |
| Approach: | They propose a task where humans can provide free-form natural language feedback to correct a system when it generates an inaccurate interpretation of an initial utterance. |
| Outcome: | The proposed model improves the parsing accuracy while maintaining flexibility of natural language interaction. |
GEAR: A Scalable and Interpretable Evaluation Framework for RAG-Based Car Assistant Systems (2025.emnlp-industry)
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Niloufar Beyranvand, Hamidreza Dastmalchi, Aijun An, Heidar Davoudi, Winston Chan, Ron DiCarlantonio
| Challenge: | Large language models (LLMs) increasingly power car assistants, but evaluating response quality remains a challenge. |
| Approach: | They propose a framework that uses large language models as evaluators to compare assistant responses against ground-truth counterparts. |
| Outcome: | The proposed framework compares assistant responses against ground-truth counterparts, assessing coverage, correctness, and other dimensions of answer quality. |
Rethinking Personality Assessment from Human-Agent Dialogues: Fewer Rounds May Be Better Than More (2025.findings-emnlp)
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| Challenge: | Existing personality assessment datasets based on natural language do not consider interactivity. |
| Approach: | They propose to use a Chinese dataset to study the effects of different interaction rounds and agent personalities on personality assessment. |
| Outcome: | The proposed dataset contains 1260 interaction rounds between humans and agents with different personalities. |
UnrealLLM: Towards Highly Controllable and Interactable 3D Scene Generation by LLM-powered Procedural Content Generation (2025.findings-acl)
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SongTang SongTang, Kaiyong Zhao, Lei Wang, Yuliang Li, Xuebo Liu, Junyi Zou, Qiang Wang, Xiaowen Chu
| Challenge: | UnrealLLM is a novel framework that connects natural language descriptions with the professional PCG system (Unreal Engine 5) to automate scene generation. |
| Approach: | They propose a novel multi-agent framework that connects natural language descriptions with the professional PCG system (Unreal Engine 5) to automate scene generation. |
| Outcome: | The proposed framework achieves competitive performance in technical metrics and aesthetic quality, offering unique advantages in generation scale and interactivity. |