most citedMulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation

5 citations · 8 across the 3 of their papers we have counts for

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cs.CL20241 cited

PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in Left-Behind Children

Yiqun Zhang, Xiaocui Yang, Xiaobai Li +5

Left-behind children (LBCs), numbering over 66 million in China, face severe mental health challenges due to parental migration for work. Early screening and identification of at-r…

cs.CL20241 cited

Language Models as Continuous Self-Evolving Data Engineers

Peidong Wang, Ming Wang, Zhiming Ma +5

Large Language Models (LLMs) have demonstrated remarkable capabilities on various tasks, while the further evolvement is limited to the lack of high-quality training data. In addit…

cs.CL2024

ChatZero:Zero-shot Cross-Lingual Dialogue Generation via Pseudo-Target Language

Yongkang Liu, Feng Shi, Daling Wang +2

Although large language models(LLMs) show amazing capabilities, among various exciting applications discovered for LLMs fall short in other low-resource languages. Besides, most ex…

cs.CL2024

A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generation

Yongkang Liu, Ercong Nie, Shi Feng +5

Current state-of-the-art dialogue systems heavily rely on extensive training datasets. However, challenges arise in domains where domain-specific training datasets are insufficient…

cs.CL20233 cited

MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks

Xiaocui Yang, Wenfang Wu, Shi Feng +7

The popularity of multimodal large language models (MLLMs) has triggered a recent surge in research efforts dedicated to evaluating these models. Nevertheless, existing evaluation…

cs.CL20225 cited

MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation

Yongkang Liu, Shi Feng, Daling Wang +1

Building dialogue generation systems in a zero-shot scenario remains a huge challenge, since the typical zero-shot approaches in dialogue generation rely heavily on large-scale pre…