# Instance Relation Learning Network with Label Knowledge Propagation for Few-shot Multi-label Intent Detection

arXiv: https://arxiv.org/abs/2510.07776 · HTML: https://mlsanity.com/paper/2510.07776/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 46 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [Meta-Learning with Differentiable Convex Optimization](https://mlsanity.com/paper/1904.03758/) (2019)
- [Meta Networks](https://mlsanity.com/paper/1703.00837/) (2017)
- [Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context](https://mlsanity.com/paper/1911.10484/) (2019)
- [ProtGNN: Towards Self-Explaining Graph Neural Networks](https://mlsanity.com/paper/2112.00911/) (2021)
- [Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching](https://mlsanity.com/paper/1911.11308/) (2019)
- [Learning Normal Dynamics in Videos with Meta Prototype Network](https://mlsanity.com/paper/2104.06689/) (2021)
- [CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors](https://mlsanity.com/paper/2305.05711/) (2023)
- [Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection](https://mlsanity.com/paper/2203.15793/) (2022)
- [Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning](https://mlsanity.com/paper/2109.06349/) (2021)
- [Extracting Class Activation Maps from Non-Discriminative Features as well](https://mlsanity.com/paper/2303.10334/) (2023)
- [Self-supervised Hypergraph Representation Learning for Sociological Analysis](https://mlsanity.com/paper/2212.11440/) (2022)
- [BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning](https://mlsanity.com/paper/2306.13101/) (2023)
- [Few-shot Learning for Multi-label Intent Detection](https://mlsanity.com/paper/2010.05256/) (2020)
- [Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization](https://mlsanity.com/paper/2205.07208/) (2022)
- [Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention](https://mlsanity.com/paper/2112.01037/) (2021)
- [Hybrid Graph Neural Networks for Few-Shot Learning](https://mlsanity.com/paper/2112.06538/) (2021)
- [Multi-Label Few-Shot Learning for Aspect Category Detection](https://mlsanity.com/paper/2105.14174/) (2021)
- [Dequantified Diffusion-Schr"odinger Bridge for Density Ratio Estimation](https://mlsanity.com/paper/2505.05034/) (2025)
- [TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speed](https://mlsanity.com/paper/2203.10335/) (2022)
- [Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection](https://mlsanity.com/paper/2206.13980/) (2022)
- [Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation](https://mlsanity.com/paper/2308.14654/) (2023)
- [MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention](https://mlsanity.com/paper/2312.05741/) (2023)
- [Boosting Model Resilience via Implicit Adversarial Data Augmentation](https://mlsanity.com/paper/2404.16307/) (2024)
- [Few-shot Adaptation Works with UnpredicTable Data](https://mlsanity.com/paper/2208.01009/) (2022)
- [Label-Driven Denoising Framework for Multi-Label Few-Shot Aspect Category Detection](https://mlsanity.com/paper/2210.04220/) (2022)
- [QAID: Question Answering Inspired Few-shot Intent Detection](https://mlsanity.com/paper/2303.01593/) (2023)
- [All Labels Together: Low-shot Intent Detection with an Efficient Label Semantic Encoding Paradigm](https://mlsanity.com/paper/2309.03563/) (2023)

## Abstract (arXiv)

Few-shot Multi-label Intent Detection (MID) is crucial for dialogue systems, aiming to detect multiple intents of utterances in low-resource dialogue domains. Previous studies focus on a two-stage pipeline. They first learn representations of utterances with multiple labels and then use a threshold-based strategy to identify multi-label results. However, these methods rely on representation classification and ignore instance relations, leading to error propagation. To solve the above issues, we propose a multi-label joint learning method for few-shot MID in an end-to-end manner, which constructs an instance relation learning network with label knowledge propagation to eliminate error propagation. Concretely, we learn the interaction relations between instances with class information to propagate label knowledge between a few labeled (support set) and unlabeled (query set) instances. With label knowledge propagation, the relation strength between instances directly indicates whether two utterances belong to the same intent for multi-label prediction. Besides, a dual relation-enhanced loss is developed to optimize support- and query-level relation strength to improve performance. Experiments show that we outperform strong baselines by an average of 9.54% AUC and 11.19% Macro-F1 in 1-shot scenarios.