Scaling laws for downstream task performance of large language models
Scaling laws provide important insights that can guide the design of large language models
(LLMs). Existing work has primarily focused on studying scaling laws for pretraining …
(LLMs). Existing work has primarily focused on studying scaling laws for pretraining …
Collaborative Performance Prediction for Large Language Models
Comprehensively understanding and accurately predicting the performance of large
language models across diverse downstream tasks has emerged as a pivotal challenge in …
language models across diverse downstream tasks has emerged as a pivotal challenge in …
Granularity is crucial when applying differential privacy to text: An investigation for neural machine translation
DNL Vu, T Igamberdiev, I Habernal - arXiv preprint arXiv:2407.18789, 2024 - arxiv.org
Applying differential privacy (DP) by means of the DP-SGD algorithm to protect individual
data points during training is becoming increasingly popular in NLP. However, the choice of …
data points during training is becoming increasingly popular in NLP. However, the choice of …