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Beyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language Models
Sangmin Song, Juhwan Choi, JungMin Yun +1
Large language models (LLMs) have demonstrated remarkable performance in zero-shot dialogue state tracking (DST), reducing the need for task-specific training. However, conventiona…
Plug-in and Fine-tuning: Bridging the Gap between Small Language Models and Large Language Models
Kyeonghyun Kim, Jinhee Jang, Juhwan Choi +3
Large language models (LLMs) are renowned for their extensive linguistic knowledge and strong generalization capabilities, but their high computational demands make them unsuitable…
Adverb Is the Key: Simple Text Data Augmentation with Adverb Deletion
Juhwan Choi, YoungBin Kim
In the field of text data augmentation, rule-based methods are widely adopted for real-world applications owing to their cost-efficiency. However, conventional rule-based approache…
SoftEDA: Rethinking Rule-Based Data Augmentation with Soft Labels
Juhwan Choi, Kyohoon Jin, Junho Lee +2
Rule-based text data augmentation is widely used for NLP tasks due to its simplicity. However, this method can potentially damage the original meaning of the text, ultimately hurti…
GPTs Are Multilingual Annotators for Sequence Generation Tasks
Juhwan Choi, Eunju Lee, Kyohoon Jin +1
Data annotation is an essential step for constructing new datasets. However, the conventional approach of data annotation through crowdsourcing is both time-consuming and expensive…