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Found 8 papers, 1 papers with code
Overcoming Catastrophic Forgetting During Domain Adaptation of Seq2seq Language Generation
Seq2seq language generation models that are trained offline with multiple domains in a sequential fashion often suffer from catastrophic forgetting.
Joint Goal Segmentation and Goal Success Prediction on Multi-Domain Conversations
To evaluate the performance of a multi-domain goal-oriented Dialogue System (DS), it is important to understand what the users’ goals are for the conversations and whether those goals are successfully achieved.
MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context Learning
Large Language models (LLMs) have demonstrated impressive in-context learning (ICL) capabilities, where a LLM makes predictions for a given test input together with a few input-output pairs (demonstrations).
A Self-Healing Magnetic-Array-Type Current Sensor with Data-Driven Identification of Abnormal Magnetic Measurement Units
However, the susceptibility of the measurement error of one or more magnetic measurement units (MMUs) within the current sensor to drift significantly from the nominal value due to environmental factors poses a potential threat to the measurement accuracy of the current sensor. In light of the need to ensure sustained measurement accuracy over the long term, this paper proposes an innovative self-healing approach rooted in cyber-physics correlation.
PersonaPKT: Building Personalized Dialogue Agents via Parameter-efficient Knowledge Transfer
Personalized dialogue agents (DAs) powered by large pre-trained language models (PLMs) often rely on explicit persona descriptions to maintain personality consistency.
Query Expansion and Entity Weighting for Query Reformulation Retrieval in Voice Assistant Systems
However, these methods rarely focus on query expansion and entity weighting simultaneously, which may limit the scope and accuracy of the query reformulation retrieval.
