Retrieval-augmented few-shot text classification

G Yu, L Liu, H Jiang, S Shi, X Ao - Findings of the Association for …, 2023 - aclanthology.org
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 …

Synthdst: Synthetic data is all you need for few-shot dialog state tracking

A Kulkarni, BH Tseng, JRA Moniz… - arxiv preprint arxiv …, 2024 - arxiv.org
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 …

Retrieval-Enhanced Machine Learning: Synthesis and Opportunities

F Diaz, A Drozdov, TE Kim, A Salemi… - Proceedings of the 2024 …, 2024 - dl.acm.org
Retrieval-enhanced machine learning (REML) refers to the use of information retrieval
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

JD Finch, JD Choi - arxiv preprint arxiv:2405.12468, 2024 - arxiv.org
We demonstrate substantial performance gains in zero-shot dialogue state tracking (DST) by
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

X Dong, Y Feng, Z Lu, G Shi, XM Wu - Proceedings of the 2024 …, 2024 - aclanthology.org
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 …