research
∙
08/01/2023
Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models
Today, large language models (LLMs) are taught to use new tools by provi...
share
research
∙
06/26/2023
SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality
In the last year alone, a surge of new benchmarks to measure composition...
research
∙
05/03/2023
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
Deploying large language models (LLMs) is challenging because they are m...
research
∙
05/25/2022
Understanding Programmatic Weak Supervision via Source-aware Influence Function
Programmatic Weak Supervision (PWS) aggregates the source votes of multi...
research
∙
03/02/2022
Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming
Weak Supervision (WS) techniques allow users to efficiently create large...
research
∙
02/11/2022
A Survey on Programmatic Weak Supervision
Labeling training data has become one of the major roadblocks to using m...
research
∙
09/29/2021
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach
The goal of multi-label learning (MLL) is to associate a given instance ...
research
∙
05/31/2020
Evaluations and Methods for Explanation through Robustness Analysis
Among multiple ways of interpreting a machine learning model, measuring ...
research
∙
01/27/2019
