57 citations · 62 across the 5 of their papers we have counts for
5 papers
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…
Exploiting Abstract Meaning Representation for Open-Domain Question Answering
Cunxiang Wang, Zhikun Xu, Qipeng Guo +4
The Open-Domain Question Answering (ODQA) task involves retrieving and subsequently generating answers from fine-grained relevant passages within a database. Current systems levera…
Investigating Forgetting in Pre-Trained Representations Through Continual Learning
Yun Luo, Zhen Yang, Xuefeng Bai +3
Representation forgetting refers to the drift of contextualized representations during continual training. Intuitively, the representation forgetting can influence the general know…
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…
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…