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Found 8 papers, 1 papers with code
Learning and Evaluating a Differentially Private Pre-trained Language Model
One method to guarantee the privacy of such individuals is to train a differentially-private language model, but this usually comes at the expense of model performance.
TextGenSHAP: Scalable Post-hoc Explanations in Text Generation with Long Documents
Given their nature as black-boxes using complex reasoning processes on their inputs, it is inevitable that the demand for scalable and faithful explanations for LLMs' generated content will continue to grow.
SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data
Text-to-SQL aims to automate the process of generating SQL queries on a database from natural language text.
SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)
Text-to-SQL, the process of translating natural language into Structured Query Language (SQL), represents a transformative application of large language models (LLMs), potentially revolutionizing how humans interact with data.
Universal Self-Adaptive Prompting
A hallmark of modern large language models (LLMs) is their impressive general zero-shot and few-shot abilities, often elicited through in-context learning (ICL) via prompting.
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
Third, we reduce both the model size and the amount of data required to outperform LLMs; our finetuned 770M T5 model outperforms the few-shot prompted 540B PaLM model using only 80% of available data on a benchmark, whereas standard finetuning the same T5 model struggles to match even by using 100% of the dataset.
Controlling Commercial Cooling Systems Using Reinforcement Learning
This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems.
Interpretable Sequence Learning for COVID-19 Forecasting
We propose a novel approach that integrates machine learning into compartmental disease modeling to predict the progression of COVID-19.
