Papers Intent Discovery
“Intent Discovery” 태그가 달린 논문 45편 · 필터 해제
SkillFuzz: Fuzzing Skill Composition for Implicit Intents Discovery in Open Skill Marketplaces
Large Language Model (LLM)-based agents increasingly automate software engineering tasks through reusable skills, natural-language instruction documents that guide planning and execution. Open skill marketplaces enable u…
Intent DiscoveryTextClusterLab: An Integrated Framework for Reliable Text Clustering Studies
In recent years, text clustering has become a critical technique for applications including intent discovery, topic mining, and recommendation systems. However, evaluating text clustering algorithms remains challenging s…
Recommendation SystemsIntent DiscoveryText ClusteringNILC: Discovering New Intents with LLM-assisted Clustering
New intent discovery (NID) seeks to recognize both new and known intents from unlabeled user utterances, which finds prevalent use in practical dialogue systems. Existing works towards NID mainly adopt a cascaded archite…
Intent DiscoveryIntegration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework
Intent detection aims to identify user intents from natural language inputs, where supervised methods rely heavily on labeled in-domain (IND) data and struggle with out-of-domain (OOD) intents, limiting their practical a…
Domain AdaptationIntent DetectionIntent DiscoveryFrom Intent Discovery to Recognition with Topic Modeling and Synthetic Data
Understanding and recognizing customer intents in AI systems is crucial, particularly in domains characterized by short utterances and the cold start problem, where recommender systems must include new products or servic…
DiversityIntent DiscoveryIntent RecognitionRecommendation Systems+1LANID: LLM-assisted New Intent Discovery
Task-oriented Dialogue Systems (TODS) often face the challenge of encountering new intents. New Intent Discovery (NID) is a crucial task that aims to identify these novel intents while maintaining the capability to recog…
Intent DiscoveryTask-Oriented Dialogue SystemsTripletDial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialogues
Discovering customer intentions in dialogue conversations is crucial for automated service agents. However, existing intent clustering methods often fail to align with human perceptions due to a heavy reliance on embeddi…
ClusteringCoherence EvaluationIntent DiscoveryText ClusteringIntentGPT: Few-shot Intent Discovery with Large Language Models
In today's digitally driven world, dialogue systems play a pivotal role in enhancing user interactions, from customer service to virtual assistants. In these dialogues, it is important to identify user's goals automatica…
In-Context LearningIntent DetectionIntent DiscoveryKULCQ: An Unsupervised Keyword-based Utterance Level Clustering Quality Metric
Intent discovery is crucial for both building new conversational agents and improving existing ones. While several approaches have been proposed for intent discovery, most rely on clustering to group similar utterances t…
ClusteringIntent DiscoveryAuto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents
In this paper, we introduce Auto-Intent, a method to adapt a pre-trained large language model (LLM) as an agent for a target domain without direct fine-tuning, where we empirically focus on web navigation tasks. Our appr…
Decision MakingIntent DiscoveryLanguage ModelingLanguage Modelling+1Pseudo-Label Enhanced Prototypical Contrastive Learning for Uniformed Intent Discovery
New intent discovery is a crucial capability for task-oriented dialogue systems. Existing methods focus on transferring in-domain (IND) prior knowledge to out-of-domain (OOD) data through pre-training and clustering stag…
ClusteringContrastive LearningIntent DiscoveryOpen Intent Discovery+3Assured Automatic Programming via Large Language Models
With the advent of AI-based coding engines, it is possible to convert natural language requirements to executable code in standard programming languages. However, AI-generated code can be unreliable, and the natural lang…
Intent DiscoveryA Survey of Ontology Expansion for Conversational Understanding
In the rapidly evolving field of conversational AI, Ontology Expansion (OnExp) is crucial for enhancing the adaptability and robustness of conversational agents. Traditional models rely on static, predefined ontologies, …
Intent DiscoverySurveyControllable Discovery of Intents: Incremental Deep Clustering Using Semi-Supervised Contrastive Learning
Deriving value from a conversational AI system depends on the capacity of a user to translate the prior knowledge into a configuration. In most cases, discovering the set of relevant turn-level speaker intents is often o…
ClusteringContinual LearningContrastive LearningDeep Clustering+2One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learning
This paper presents a novel and comprehensive solution to enhance both the robustness and efficiency of question answering (QA) systems through supervised contrastive learning (SCL). Training a high-performance QA system…
Continual LearningContrastive Learningintent-classificationIntent Classification+4Towards Real-world Scenario: Imbalanced New Intent Discovery
New Intent Discovery (NID) aims at detecting known and previously undefined categories of user intent by utilizing limited labeled and massive unlabeled data. Most prior works often operate under the unrealistic assumpti…
Intent DiscoveryRepresentation LearningRoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations
New Intent Discovery (NID) strives to identify known and reasonably deduce novel intent groups in the open-world scenario. But current methods face issues with inaccurate pseudo-labels and poor representation learning, c…
Contrastive LearningIntent DiscoveryPseudo LabelRepresentation LearningNew Intent Discovery with Attracting and Dispersing Prototype
New Intent Discovery (NID) aims to recognize known and infer new intent categories with the help of limited labeled and large-scale unlabeled data. The task is addressed as a feature-clustering problem and recent studies…
Intent DiscoveryLanguage ModelingLanguage ModellingLarge Language ModelIntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery
Identifying intents from dialogue utterances forms an integral component of task-oriented dialogue systems. Intent-related tasks are typically formulated either as a classification task, where the utterances are classifi…
Contrastive Learningintent-classificationIntent ClassificationIntent Detection+3A Diffusion Weighted Graph Framework for New Intent Discovery
New Intent Discovery (NID) aims to recognize both new and known intents from unlabeled data with the aid of limited labeled data containing only known intents. Without considering structure relationships between samples,…
Contrastive LearningIntent Discovery