Papers Intent Classification
“Intent Classification” 태그가 달린 논문 375편 · 필터 해제
UpgradeBench: A Decision-Centric Benchmark for Upgrading Fine-Tuned LLM Specialists
Organizations maintain task-specific adapters for open-weight language models, and each new base-model release forces a migration decision: retain existing specialists, port adapters, refresh from preserved behavior, or …
Intent ClassificationDziri Voicebot: An End-to-End Low-Resource Speech-to-Speech Conversational System for Algerian Dialect
Automatic speech and language technologies are still heavily biased toward high-resource languages, limiting their applicability to dialectal and low-resource settings such as Algerian Dialect. This language presents add…
Natural Language UnderstandingText-To-Speech SynthesisIntent ClassificationResponse GenerationThe Significance of Style Diversity in Annotation-Free Synthetic Data Generation
Generating high-utility synthetic data for intent classification typically requires human-annotated seed data, which is often unavailable in fast-paced industrial settings. In this paper, we propose a framework for synth…
Synthetic Data GenerationIntent ClassificationDialogue GenerationDroneShield-AI: A Multi-Modal Sensor Fusion Framework for Real-Time Autonomous Drone Threat Detection, Behavioral Intent Classification, and Swarm Intelligence in Contested Airspace
Unmanned Aerial Vehicle (UAV) threats have emerged as a defining security challenge of the 21st century. This paper presents DroneShield-AI, a unified open framework integrating six processing layers: RF signal classific…
Intent ClassificationGraph Neural NetworkDisentangling Adversarial Prompts: A Semantic-Graph Defense for Robust LLM Security
Large Language Models (LLMs) are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulting in harmful or inappropriate outputs. Such attacks, including jailbr…
Computational EfficiencyIntent ClassificationIntent DetectionAI-Care: A Conversational Agentic System for Task Coordination in Alzheimer's Disease Care
Individuals with Alzheimer's disease (AD) and Alzheimer's disease-related dementia (ADRD) experience memory and thinking changes that impact their ability to use digital daily management tools. For example, adding an eve…
Intent ClassificationDensity Field State Space Models: 1-Bit Distillation, Efficient Inference, and Knowledge Organization in Mamba-2
We present Density Field State Space Models (DF-SSM), a framework for compressing SSMs to a 1-bit scaffold with int8 low-rank correction. Applied to Mamba-2 1.3B, we achieve a 278 MB model (9.7x smaller than the 2.7 GB F…
Intent ClassificationltzGLUE: Luxembourgish General Language Understanding Evaluation
This paper presents ltzGLUE, the first Natural Language Understanding (NLU) benchmark for Luxembourgish (LTZ) based on the popular GLUE benchmark for English. Although NLU tasks are available for many European languages …
Natural Language UnderstandingMulti-class ClassificationIntent ClassificationPioneer Agent: Continual Improvement of Small Language Models in Production
Small language models are attractive for production deployment due to their low cost, fast inference, and ease of specialization. However, adapting them to a specific task remains a challenging engineering loop, driven n…
Intent ClassificationCode GenerationCivicShield: A Cross-Domain Defense-in-Depth Framework for Securing Government-Facing AI Chatbots Against Multi-Turn Adversarial Attacks
LLM-based chatbots in government services face critical security gaps. Multi-turn adversarial attacks achieve over 90% success against current defenses, and single-layer guardrails are bypassed with similar rates. We pre…
Intent ClassificationAnomaly DetectionFrom Synthetic to Native: Benchmarking Multilingual Intent Classification in Logistics Customer Service
Multilingual intent classification is central to customer-service systems on global logistics platforms, where models must process noisy user queries across languages and hierarchical label spaces. Yet most existing mult…
Cross-Lingual TransferIntent ClassificationA Comparative Empirical Study of Catastrophic Forgetting Mitigation in Sequential Task Adaptation for Continual Natural Language Processing Systems
Neural language models deployed in real-world applications must continually adapt to new tasks and domains without forgetting previously acquired knowledge. This work presents a comparative empirical study of catastrophi…
Intent ClassificationContinual LearningBeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding
Integrating large language models (LLMs) into wireless communication optimization is a promising yet challenging direction. Existing approaches either use LLMs as black-box solvers or code generators, tightly coupling th…
Intent ClassificationSpatial ReasoningUncertainty Estimation for the Open-Set Text Classification systems
Accurate uncertainty estimation is essential for building robust and trustworthy recognition systems. In this paper, we consider the open-set text classification (OSTC) task - and uncertainty estimation for it. For OSTC …
Intent ClassificationText ClassificationMALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation Detection
The intentional creation and spread of disinformation poses a significant threat to public discourse. However, existing English datasets and research rarely address the intentionality behind the disinformation. This work…
Intent ClassificationFrom Natural Language to PromQL: A Catalog-Driven Framework with Dynamic Temporal Resolution for Cloud-Native Observability
Modern cloud-native platforms expose thousands of time series metrics through systems like Prometheus, yet formulating correct queries in domain-specific languages such as PromQL remains a significant barrier for platfor…
Intent ClassificationMeta-Sel: Efficient Demonstration Selection for In-Context Learning via Supervised Meta-Learning
Demonstration selection is a practical bottleneck in in-context learning (ICL): under a tight prompt budget, accuracy can change substantially depending on which few-shot examples are included, yet selection must remain …
Reinforcement LearningIntent ClassificationPrompt EngineeringHoverAI: An Embodied Aerial Agent for Natural Human-Drone Interaction
Drones operating in human-occupied spaces suffer from insufficient communication mechanisms that create uncertainty about their intentions. We present HoverAI, an embodied aerial agent that integrates drone mobility, inf…
Intent ClassificationMANTRA: a Framework for Multi-stage Adaptive Noise TReAtment During Training
The reliable application of deep learning models to software engineering tasks hinges on high-quality training data. Yet, large-scale repositories inevitably introduce noisy or mislabeled examples that degrade both accur…
Intent ClassificationTransformer-Driven Triple Fusion Framework for Enhanced Multimodal Author Intent Classification in Low-Resource Bangla
The expansion of the Internet and social networks has led to an explosion of user-generated content. Author intent understanding plays a crucial role in interpreting social media content. This paper addresses author inte…
Intent Classification