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cs.CL2023
Hint-enhanced In-Context Learning wakes Large Language Models up for knowledge-intensive tasks
Yifan Wang, Qingyan Guo, Xinzhe Ni +4
In-context learning (ICL) ability has emerged with the increasing scale of large language models (LLMs), enabling them to learn input-label mappings from demonstrations and perform…
cs.CL2023★ 1 cited
Specialist or Generalist? Instruction Tuning for Specific NLP Tasks
Chufan Shi, Yixuan Su, Cheng Yang +2
The potential of large language models (LLMs) to simultaneously perform a wide range of natural language processing (NLP) tasks has been the subject of extensive research. Although…
cs.CL2023
Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning
Hengyuan Zhang, Dawei Li, Yanran Li +3
The standard definition generation task requires to automatically produce mono-lingual definitions (e.g., English definitions for English words), but ignores that the generated def…