Extrapolating to Unnatural Language Processing with GPT-3’s In-context Learning: The Good, the Bad, and the Mysterious

Linking pages

How does in-context learning work? A framework for understanding the differences from traditional supervised learning | SAIL Blog https://ai.stanford.edu/blog/understanding-incontext/ 0 comments

Linked pages

[2005.14165] Language Models are Few-Shot Learners https://arxiv.org/abs/2005.14165 201 comments
GPT-3 Creative Fiction · Gwern.net https://www.gwern.net/GPT-3#prompts-as-programming 141 comments
OpenAI https://openai.com/ 134 comments
[1801.00631] Deep Learning: A Critical Appraisal https://arxiv.org/abs/1801.00631 102 comments
[1911.01547] On the Measure of Intelligence https://arxiv.org/abs/1911.01547 37 comments
OpenAI API https://openai.com/blog/openai-api/ 25 comments
Sharif Shameem on Twitter: "This is mind blowing. With GPT-3, I built a layout generator where you just describe any layout you want, and it generates the JSX code for you. W H A T https://t.co/w8JkrZO4lk" / Twitter https://twitter.com/sharifshameem/status/1282676454690451457 19 comments
[2105.11447] True Few-Shot Learning with Language Models https://arxiv.org/abs/2105.11447 7 comments
https://arxiv.org/abs/2101.03961 4 comments
NeurIPS 2020 : Orals & Spotlights Track 03: Language/Audio Applications https://neurips.cc/virtual/2020/public/session_oral_21097.html 1 comment
Dynabench https://dynabench.org/ 1 comment
[2010.03648] A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks https://arxiv.org/abs/2010.03648 1 comment
[2104.04473] Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM https://arxiv.org/abs/2104.04473 1 comment
[2001.08361] Scaling Laws for Neural Language Models https://arxiv.org/abs/2001.08361 0 comments
GitHub - google/BIG-bench: Beyond the Imitation Game collaborative benchmark for measuring and extrapolating the capabilities of language models https://github.com/google/BIG-bench 0 comments
[2102.09690] Calibrate Before Use: Improving Few-Shot Performance of Language Models https://arxiv.org/abs/2102.09690 0 comments
[2009.07118] It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners https://arxiv.org/abs/2009.07118 0 comments
[2102.01293] Scaling Laws for Transfer https://arxiv.org/abs/2102.01293 0 comments
https://huggingface.co/transformers/summary.html 0 comments