"Galactica" is a large scientific language model with 120B parameters, that can be used for a lot of tasks including
⦿ Predicting citations
⦿ Generating literature review
⦿ Generating molecules ⦿ Generating Jupiter notebooks etc.,
RESEARCH
-
Galactica: 120B Scientific Language Model for Research
By
–
-
Galactica LLM Performance with XGBoost Model Implementation
By
–
Amazed by the performance of Galactica by @paperswithcode
— SRK (@sudalairajkumar) 16 novembre 2022
Tried with prompt "jupyter notebook on how to use xgboost model" @tunguz might like it 🙂 #LLM #NLP #AI pic.twitter.com/7ohrREfiltAmazed by the performance of Galactica by @paperswithcode Tried with prompt "jupyter notebook on how to use xgboost model" @tunguz might like it 🙂 #LLM #NLP #AI
-
Large Language Models Are Not Zero-Shot Communicators
By
–
Large language models are not zero-shot communicators Ruis et al.: https://
arxiv.org/abs/2210.14986 #Artificialintelligence #DeepLearning #MachineLearning -
Iterative Denoising: Simple Code, Core Idea
By
–
like! tiny idea tiny code, strips away the formalism except the high level idea (iterative denoising on a schedule)
-
How Technologies Become Standard: Adoption Process Framework
By
–
first they do it as part of their job then they do it as a stepping stone then it wins
-
Data Quality and Curriculum Learning for LLM Training
By
–
"Obviously anything that looks useless (like SHA hashes or other noise) is not worth training on and is just wasting training capacity and time"
"You may want to start with simpler topics and work up to more complex later, just like in human school" -
Meta-learning policies for LLM attention management during training
By
–
Feels like a lot of fertile ground is left in managing the "attention" of an LLM during its training via a meta-learning policy, instead of the typical "memorize dataset uniformly at random" strategy. And giving it a calculator and a scratch pad.
-
Training Strategies: Skimming, Filtering Noise, and Revisiting Content
By
–
More generally a few remarkable strategies people use during their training:
1) skim text because they already know it
2) ignore text because it's clearly noise (e.g. they won't memorize SHA256 hashes. LLMs will.)
3) revisit parts that are learnable but not yet learned -
Examples vs. Presentations: Spaced Repetition in LLM Training
By
–
Is it the number of examples that matters or the number of presentations to the model during training? E.g. humans used spaced repetition to memorize facts but there are no equivalents of similar techniques in LLMs where the typical training regime is uniform random.
-
Stanford HAI Spring Conference 2023: AI, Creativity, Policy
By
–
A sneak preview of the @StanfordHAI Spring Conference 2023 – AI, generative models, arts, creativity, policy, regulation, and more! Stay tuned and sign up to our mailing list: https://
hai.stanford.edu/subscribe-hai-
mailing-list
…