4 citations · 11 across the 13 of their papers we have counts for
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cs.CL2023
Turning Dust into Gold: Distilling Complex Reasoning Capabilities from LLMs by Leveraging Negative Data
Yiwei Li, Peiwen Yuan, Shaoxiong Feng +5
Large Language Models (LLMs) have performed well on various reasoning tasks, but their inaccessibility and numerous parameters hinder wide application in practice. One promising wa…
cs.CL2023
EXMODD: An EXplanatory Multimodal Open-Domain Dialogue dataset
Hang Yin, Pinren Lu, Ziang Li +2
The need for high-quality data has been a key issue hindering the research of dialogue tasks. Recent studies try to build datasets through manual, web crawling, and large pre-train…
cs.CL2023★ 1 cited
Heterogeneous-Branch Collaborative Learning for Dialogue Generation
Yiwei Li, Shaoxiong Feng, Bin Sun +1
With the development of deep learning, advanced dialogue generation methods usually require a greater amount of computational resources. One promising approach to obtaining a high-…