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Retrieval-augmented few-shot text classification
Retrieval-augmented methods are successful in the standard scenario where the retrieval
space is sufficient; whereas in the few-shot scenario with limited retrieval space, this paper …
space is sufficient; whereas in the few-shot scenario with limited retrieval space, this paper …
Synthdst: Synthetic data is all you need for few-shot dialog state tracking
In-context learning with Large Language Models (LLMs) has emerged as a promising
avenue of research in Dialog State Tracking (DST). However, the best-performing in-context …
avenue of research in Dialog State Tracking (DST). However, the best-performing in-context …
Retrieval-Enhanced Machine Learning: Synthesis and Opportunities
Retrieval-enhanced machine learning (REML) refers to the use of information retrieval
methods to support reasoning and inference in machine learning tasks. Although relatively …
methods to support reasoning and inference in machine learning tasks. Although relatively …
Diverse and effective synthetic data generation for adaptable zero-shot dialogue state tracking
We demonstrate substantial performance gains in zero-shot dialogue state tracking (DST) by
enhancing training data diversity through synthetic data generation. Existing DST datasets …
enhancing training data diversity through synthetic data generation. Existing DST datasets …
Zero-shot Cross-domain Dialogue State Tracking via Context-aware Auto-prompting and Instruction-following Contrastive Decoding
Zero-shot cross-domain dialogue state tracking (DST) enables us to manage task-oriented
dialogues in new, unseen domains without the cost of collecting in-domain data. Previous …
dialogues in new, unseen domains without the cost of collecting in-domain data. Previous …