53 citations · 183 across the 18 of their papers we have counts for
12 papers · 1 filter
In-context Continual Learning Assisted by an External Continual Learner
Saleh Momeni, Sahisnu Mazumder, Zixuan Ke +1
Existing continual learning (CL) methods mainly rely on fine-tuning or adapting large language models (LLMs). They still suffer from catastrophic forgetting (CF). Little work has b…
Continual Learning Using Only Large Language Model Prompting
Jiabao Qiu, Zixuan Ke, Bing Liu
We introduce CLOB, a novel continual learning (CL) paradigm wherein a large language model (LLM) is regarded as a black box. Learning is done incrementally via only verbal promptin…
Bridging the Preference Gap between Retrievers and LLMs
Zixuan Ke, Weize Kong, Cheng Li +3
Large Language Models (LLMs) have demonstrated superior results across a wide range of tasks, and Retrieval-augmented Generation (RAG) is an effective way to enhance the performanc…
Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks
Zixuan Ke, Bing Liu, Wenhan Xiong +2
Continual learning (CL) has two main objectives: preventing catastrophic forgetting (CF) and encouraging knowledge transfer (KT). The existing literature mainly focused on overcomi…
Continual Pre-training of Language Models
Zixuan Ke, Yijia Shao, Haowei Lin +3
Language models (LMs) have been instrumental for the rapid advance of natural language processing. This paper studies continual pre-training of LMs, in particular, continual domain…
Adapting a Language Model While Preserving its General Knowledge
Zixuan Ke, Yijia Shao, Haowei Lin +3
Domain-adaptive pre-training (or DA-training for short), also known as post-training, aims to train a pre-trained general-purpose language model (LM) using an unlabeled corpus of a…
Continual Learning of Natural Language Processing Tasks: A Survey
Zixuan Ke, Bing Liu
Continual learning (CL) is a learning paradigm that emulates the human capability of learning and accumulating knowledge continually without forgetting the previously learned knowl…
Continual Training of Language Models for Few-Shot Learning
Zixuan Ke, Haowei Lin, Yijia Shao +3
Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. Adapting or posttraining an LM using an unlabeled domain corpus can pr…
Continual Learning with Knowledge Transfer for Sentiment Classification
Zixuan Ke, Bing Liu, Hao Wang +1
This paper studies continual learning (CL) for sentiment classification (SC). In this setting, the CL system learns a sequence of SC tasks incrementally in a neural network, where…
Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks
Zixuan Ke, Hu Xu, Bing Liu
This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks. Although some CL techniques have been proposed for document sentiment class…
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks
Zixuan Ke, Bing Liu, Hu Xu +1
This paper studies continual learning (CL) of a sequence of aspect sentiment classification(ASC) tasks in a particular CL setting called domain incremental learning (DIL). Each tas…
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning
Zixuan Ke, Bing Liu, Nianzu Ma +2
Continual learning (CL) learns a sequence of tasks incrementally with the goal of achieving two main objectives: overcoming catastrophic forgetting (CF) and encouraging knowledge t…