Intent Detection
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Benchmarks
ATIS
MixSNIPS
MixATIS
SNIPS
ASOS.com user intent
BANKING77
ATIS (vi)
BANKING77 10-shot
BANKING77 5-shot
CAIS
CLINC150
CLINC150 10-shot
CLINC150 5-shot
HWU64
HWU64 10-shot
HWU64 5-shot
MULTIWOZ 2.2
ProSLU
Most implemented
BERT for Joint Intent Classification and Slot Filling
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
Efficient Intent Detection with Dual Sentence Encoders
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
Zero-shot User Intent Detection via Capsule Neural Networks
Joint Slot Filling and Intent Detection via Capsule Neural Networks
Papers
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 Detection