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09/07/2023
Large Language Models as Optimizers
Optimization is ubiquitous. While derivative-based algorithms have been ...
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08/07/2023
Simple synthetic data reduces sycophancy in large language models
Sycophancy is an undesirable behavior where models tailor their response...
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05/29/2023
Brainformers: Trading Simplicity for Efficiency
Transformers are central to recent successes in natural language process...
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05/17/2023
DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining
The mixture proportions of pretraining data domains (e.g., Wikipedia, bo...
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05/15/2023
Symbol tuning improves in-context learning in language models
We present symbol tuning - finetuning language models on in-context inpu...
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03/07/2023
Larger language models do in-context learning differently
We study how in-context learning (ICL) in language models is affected by...
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02/13/2023
Symbolic Discovery of Optimization Algorithms
We present a method to formulate algorithm discovery as program search, ...
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02/03/2023
PyGlove: Efficiently Exchanging ML Ideas as Code
The increasing complexity and scale of machine learning (ML) has led to ...
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04/15/2022
Resource-Constrained Neural Architecture Search on Tabular Datasets
The best neural architecture for a given machine learning problem depend...
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01/21/2021
PyGlove: Symbolic Programming for Automated Machine Learning
Neural networks are sensitive to hyper-parameter and architecture choice...
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01/27/2020
Towards a Human-like Open-Domain Chatbot
We present Meena, a multi-turn open-domain chatbot trained end-to-end on...
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03/07/2018
