2 citations · 4 across the 2 of their papers we have counts for
3 papers
cs.CL2023
An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
Yun Luo, Zhen Yang, Fandong Meng +3
Catastrophic forgetting (CF) is a phenomenon that occurs in machine learning when a model forgets previously learned information while acquiring new knowledge for achieving a satis…
cs.CL2022★ 2 cited
Can Offline Reinforcement Learning Help Natural Language Understanding?
Ziqi Zhang, Yile Wang, Yue Zhang +1
Pre-training has been a useful method for learning implicit transferable knowledge and it shows the benefit of offering complementary features across different modalities. Recent w…
cs.CL2022★ 2 cited
RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees
Tengxiao Liu, Qipeng Guo, Xiangkun Hu +3
Interpreting the reasoning process from questions to answers poses a challenge in approaching explainable QA. A recently proposed structured reasoning format, entailment tree, mana…