126 citations · 211 across the 8 of their papers we have counts for
5 papers · 1 filter
Crawling the Internal Knowledge-Base of Language Models
Roi Cohen, Mor Geva, Jonathan Berant +1
Language models are trained on large volumes of text, and as a result their parameters might contain a significant body of factual knowledge. Any downstream task performed by these…
Learning To Retrieve Prompts for In-Context Learning
Ohad Rubin, Jonathan Herzig, Jonathan Berant
In-context learning is a recent paradigm in natural language understanding, where a large pre-trained language model (LM) observes a test instance and a few training examples as it…
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)
Chen Liang, Jonathan Berant, Quoc Le +2
Extending the success of deep neural networks to natural language understanding and symbolic reasoning requires complex operations and external memory. Recent neural program induct…
Hierarchical Question Answering for Long Documents
Eunsol Choi, Daniel Hewlett, Alexandre Lacoste +3
We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models. While most…
Learning Recurrent Span Representations for Extractive Question Answering
Kenton Lee, Shimi Salant, Tom Kwiatkowski +3
The reading comprehension task, that asks questions about a given evidence document, is a central problem in natural language understanding. Recent formulations of this task have t…