Papers Intent Detection
“Intent Detection” 태그가 달린 논문 375편 · 필터 해제
A Multi-cluster Boundary Learning Method for Out-of-Scope Intent Detection via MiniLM Embedding
Intent detection is a critical task that bridges human intents and system actions in human-machine interaction systems. However, there still exist challenges for detecting out-of-scope (OOS) intents. (i) The traditional …
Multi-class ClassificationIntent DetectionIntentTune: Using user demand and personalization to resolve "unknown" query intents for e-commerce search
Understanding user intent is fundamental to delivering relevant search results in e-commerce. However, substantial fraction of real-world queries are under-specified (e.g., "watch" or "shirt"), lacking explicit attribute…
Intent DetectionDisentangling 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 DetectionTask-Adaptive Embedding Refinement via Test-time LLM Guidance
We explore the effectiveness of an LLM-guided query refinement paradigm for extending the usability of embedding models to challenging zero-shot search and classification tasks. Our approach refines the embedding represe…
Instruction FollowingIntent DetectionOne Turn Too Late: Response-Aware Defense Against Hidden Malicious Intent in Multi-Turn Dialogue
Hidden malicious intent in multi-turn dialogue poses a growing threat to deployed large language models (LLMs). Rather than exposing a harmful objective in a single prompt, increasingly capable attackers can distribute t…
Intent DetectionMISID: A Multimodal Multi-turn Dataset for Complex Intent Recognition in Strategic Deception Games
Understanding human intent in complex multi-turn interactions remains a fundamental challenge in human-computer interaction and behavioral analysis. While existing intent recognition datasets focus mainly on single utter…
Intent RecognitionIntent DetectionRePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair
Large language models (LLMs) inherently absorb harmful knowledge, misinformation, and personal data during pretraining on large-scale web corpora, with no native mechanism for selective removal. While machine unlearning …
Intent DetectionKnown Intents, New Combinations: Clause-Factorized Decoding for Compositional Multi-Intent Detection
Multi-intent detection papers usually ask whether a model can recover multiple intents from one utterance. We ask a harder and, for deployment, more useful question: can it recover new combinations of familiar intents? E…
Intent DetectionDebiasing Large Language Models toward Social Factors in Online Behavior Analytics through Prompt Knowledge Tuning
Attribution theory explains how individuals interpret and attribute others' behavior in a social context by employing personal (dispositional) and impersonal (situational) causality. Large Language Models (LLMs), trained…
Intent DetectionGUIDE: A Benchmark for Understanding and Assisting Users in Open-Ended GUI Tasks
Graphical User Interface (GUI) agents have the potential to assist users in interacting with complex software (e.g., PowerPoint, Photoshop). While prior research has primarily focused on automating user actions through c…
Intent DetectionTaigiSpeech: A Low-Resource Real-World Speech Intent Dataset and Preliminary Results with Scalable Data Mining In-the-Wild
Speech technologies have advanced rapidly and serve diverse populations worldwide. However, many languages remain underrepresented due to limited resources. In this paper, we introduce \textbf{TaigiSpeech}, a real-world …
Intent DetectionThe Confidence Gate Theorem: When Should Ranked Decision Systems Abstain?
Ranked decision systems -- recommenders, ad auctions, clinical triage queues -- must decide when to intervene in ranked outputs and when to abstain. We study when confidence-based abstention monotonically improves decisi…
Collaborative FilteringIntent DetectionVision-Language System using Open-Source LLMs for Gestures in Medical Interpreter Robots
Effective communication is vital in healthcare, especially across language barriers, where non-verbal cues and gestures are critical. This paper presents a privacy-preserving vision-language framework for medical interpr…
Computational EfficiencyGesture GenerationIntent DetectionCross-lingual Matryoshka Representation Learning across Speech and Text
Speakers of under-represented languages face both a language barrier, as most online knowledge is in a few dominant languages, and a modality barrier, since information is largely text-based while many languages are prim…
Representation LearningIntent DetectionA Generative Model for Joint Multiple Intent Detection and Slot Filling
In task-oriented dialogue systems, spoken language understanding (SLU) is a critical component, which consists of two sub-tasks, intent detection and slot filling. Most existing methods focus on the single-intent SLU, wh…
Task-Oriented Dialogue SystemsSpoken Language UnderstandingMulti-Task LearningIntent DetectionReactEMG Stroke: Healthy-to-Stroke Few-shot Adaptation for sEMG-Based Intent Detection
Surface electromyography (sEMG) is a promising control signal for assist-as-needed hand rehabilitation after stroke, but detecting intent from paretic muscles often requires lengthy, subject-specific calibration and rema…
Intent DetectionInterpretability of the Intent Detection Problem: A New Approach
Intent detection, a fundamental text classification task, aims to identify and label the semantics of user queries, playing a vital role in numerous business applications. Despite the dominance of deep learning technique…
Text ClassificationIntent DetectionHow DDAIR you? Disambiguated Data Augmentation for Intent Recognition
Large Language Models (LLMs) are effective for data augmentation in classification tasks like intent detection. In some cases, they inadvertently produce examples that are ambiguous with regard to untargeted classes. We …
Intent RecognitionData AugmentationIntent DetectionCredit C-GPT: A Domain-Specialized Large Language Model for Conversational Understanding in Vietnamese Debt Collection
Debt collection is a critical function within the banking, financial services, and insurance (BFSI) sector, relying heavily on large-scale human-to-human conversational interactions conducted primarily in Vietnamese cont…
Intent DetectionBeyond Context: Large Language Models' Failure to Grasp Users' Intent
Current Large Language Models (LLMs) safety approaches focus on explicitly harmful content while overlooking a critical vulnerability: the inability to understand context and recognize user intent. This creates exploitab…
Intent RecognitionIntent Detection