ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification
This paper focuses on the task of Extreme Multi-Label Classification (XMC) whose goal is to predict multiple labels for each instance from an extremely large label space. While existing research has primarily focused on fully supervised XMC, real-world scenarios often lack supervision signals, highlighting the importance of zero-shot settings. Given the large label space, utilizing in-context learning approaches is not trivial. We address this issue by introducing In-Context Extreme Multilabel Learning (ICXML), a two-stage framework that cuts down the search space by generating a set of candidate labels through incontext learning and then reranks them. Extensive experiments suggest that ICXML advances the state of the art on two diverse public benchmarks.
Code (1)
Tasks
Extreme Multi-Label ClassificationIn-Context LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Rethinking Zero-Shot Time Series Classification: From Task-specific Classifiers to In-Context Inference
The zero-shot evaluation of time series foundation models (TSFMs) for classification typically uses a frozen encoder followed by a task-specific classifier. However, this practice violates the training-free premise of ze…
Time Series ClassificationA Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems
Recent foundation models (FMs) for zero-shot reconstruction of dynamical systems (DS) achieve strong out-of-domain generalization but provide little insight into the mechanisms that underlie their forecasts. Such an unde…
Domain GeneralizationIt's All About In-Context Learning! Teaching Extremely Low-Resource Languages to LLMs
Extremely low-resource languages, especially those written in rare scripts, as shown in Figure 1, remain largely unsupported by large language models (LLMs). This is due in part to compounding factors such as the lack of…
parameter-efficient fine-tuningContext-Aware Zero-Shot Recognition
We present a novel problem setting in zero-shot learning, zero-shot object recognition and detection in the context. Contrary to the traditional zero-shot learning methods, which simply infers unseen categories by transf…
Object RecognitionZero-Shot LearningESP-Zero: Unsupervised enhancement of zero-shot classification for Extremely Sparse Point cloud
In recent years, zero-shot learning has attracted the focus of many researchers, due to its flexibility and generality. Many approaches have been proposed to achieve the zero-shot classification of the point clouds for 3…
zero-shot-classificationZero-Shot Learning