4 citations · 7 across the 7 of their papers we have counts for
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cs.CL2024
Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning
Ahmed Elshabrawy, Yongxin Huang, Iryna Gurevych +1
While Large Language Models (LLMs) exhibit remarkable capabilities in zero-shot and few-shot scenarios, they often require computationally prohibitive sizes. Conversely, smaller Ma…
cs.CL2023★ 3 cited
Low-Resource Multi-Granularity Academic Function Recognition Based on Multiple Prompt Knowledge
Jiawei Liu, Zi Xiong, Yi Jiang +4
Fine-tuning pre-trained language models (PLMs), e.g., SciBERT, generally requires large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in…