Fetching the paper…
Reading the bibliography…
After a large language model (LLM) is deployed on edge devices, it is desirable for these devices to learn from user-generated conversation data to generate user-specific and personalized responses in real-time.
Measures of information and their applications
Jagat Narain Kapur. 1994 · 1994
Earlier work this paper cites.
Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese. 2017 · 2017
Earlier work this paper cites.
Gradient based sample selection for online continual learning
Aljundi et al. 2019a · 2019
Earlier work this paper cites.
Memory efficient experience replay for streaming learning. In 2019 International Conference on Robotics and Automation (ICRA) . IEEE
Hayes et al. 2019b · 2019
Earlier work this paper cites.
Question answering as an automatic evaluation metric for news article summarization
M. Eyal et al. 2019b · 2019
Earlier work this paper cites.
Coresets via bilevel optimization for continual learning and streaming
Borsos et al. 2020a · 2020
Earlier work this paper cites.
MedDialog: a large-scale medical dialogue dataset
Chen et al. 2020b · 2020
Earlier work this paper cites.
Self-supervised learning through the eyes of a child
Orhan et al. 2020c · 2020
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Hu et al. 2021 · 2021
Cited alongside, same era.
Finetuned language models are zero-shot learners
J. Wei et al. 2021 · 2021
Cited alongside, same era.
Enabling on-device self-supervised contrastive learning with selective data contrast. In 2021 58th ACM/IEEE Design Automation Conference (DAC) . IEEE, 655–660
Wu et al. 2021a · 2021
Cited alongside, same era.
Word alignment by fine-tuning embeddings on parallel corpora
Z. Dou et al. 2021b · 2021
Cited alongside, same era.
Rt-2: Vision-language-action models transfer web knowledge to robotic control
Brohan et al. 2023a · 2023
Closest in time.
Free Dolly: Introducing the World’s First Truly Open Instruction-Tuned LLM
C. Mike et al. 2023b · 2023
Closest in time.
Parameter-efficient fine-tuning of large-scale pre-trained language models
Ding et al. 2023c · 2023
Closest in time.
Between Reality and Delusion: Challenges of Applying Large Language Models to Companion Robots for Open-Domain Dialogues with Older Adults
Irfan et al. 2023d · 2023
Closest in time.
OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces
W. Lian et al. 2023 · 2023
Closest in time.
Federated Learning of Gboard Language Models with Differential Privacy
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Prosocialdialog: A prosocial backbone for conversational agents
Kim et al. 2022a · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Ouyang et al. 2022b · 2022
Cited alongside, same era.
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset. In Proceedings of the 57th ACL . Association for Computational Linguistics
H. Rashkin et al. 2019a
Cited in the paper.
Stanford Alpaca: An Instruction-following LLaMA model
Rohan et al. 2023e
Cited in the paper.
Xu et al. 2023 · 2023
Closest in time.
OpenLLaMA: An Open Reproduction of LLaMA
Xinyang Geng and Hao Liu. 2023 · 2023
Closest in time.