Search Results for author:
Found 5 papers, 4 papers with code
Transcending Scaling Laws with 0.1% Extra Compute
This paper proposes UL2R, a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute.
Ranked #2 on
Cross-Lingual Question Answering
on TyDiQA-GoldP
UL2: Unifying Language Learning Paradigms
Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot summarization.
Ranked #7 on
Long-range modeling
on SCROLLS
HyperPrompt: Prompt-based Task-Conditioning of Transformers
Prompt-Tuning is a new paradigm for finetuning pre-trained language models in a parameter-efficient way.
LaMDA: Language Models for Dialog Applications
We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.
Ranked #36 on
Code Generation
on HumanEval
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
Despite the recent success of multi-task learning and transfer learning for natural language processing (NLP), few works have systematically studied the effect of scaling up the number of tasks during pre-training.
