14 citations · 31 across the 16 of their papers we have counts for
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cs.CL2023★ 1 cited
Towards LLM-driven Dialogue State Tracking
Yujie Feng, Zexin Lu, Bo Liu +2
Dialogue State Tracking (DST) is of paramount importance in ensuring accurate tracking of user goals and system actions within task-oriented dialogue systems. The emergence of larg…
cs.CL2023★ 1 cited
How Good Are LLMs at Out-of-Distribution Detection?
Bo Liu, Liming Zhan, Zexin Lu +3
Out-of-distribution (OOD) detection plays a vital role in enhancing the reliability of machine learning (ML) models. The emergence of large language models (LLMs) has catalyzed a p…
cs.CL2023
Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training
Haode Zhang, Haowen Liang, Liming Zhan +2
We consider the task of few-shot intent detection, which involves training a deep learning model to classify utterances based on their underlying intents using only a small amount…