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Found 5 papers, 5 papers with code
Turn-Level Active Learning for Dialogue State Tracking
Dialogue state tracking (DST) plays an important role in task-oriented dialogue systems.
CITB: A Benchmark for Continual Instruction Tuning
In this work, we establish a CIT benchmark consisting of learning and evaluation protocols.
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment.
HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks
To address the problem, we propose HRGCN, an unsupervised deep heterogeneous graph neural network, to model complex heterogeneous relations between different entities in the system for effectively identifying these anomalous behaviour graphs.
Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics
Recent work incorporates pre-trained word embeddings such as BERT embeddings into Neural Topic Models (NTMs), generating highly coherent topics.
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