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Xiaonan He

2 accepted papers

2024

Leveraging Web-Crawled Data for High-Quality Fine-Tuning

EMNLP 2024finding

Most large language models are fine-tuned using either expensive human-annotated data or GPT-4 generated data which cannot guarantee performance in certain domains. We argue that although the web-crawled data often has formatting errors causing semantic inaccuracies, it can still serve as a valuable…

2023

Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking

AAAI 2023technical

In task-oriented dialogue systems, Dialogue State Tracking (DST) aims to extract users' intentions from the dialogue history. Currently, most existing approaches suffer from error propagation and are unable to dynamically select relevant information when utilizing previous dialogue states. Moreover,…

Cited by 7SourcePDFScholar