16 citations · 31 across the 4 of their papers we have counts for
4 papers
Retrieval-Pretrained Transformer: Long-range Language Modeling with Self-retrieval
Ohad Rubin, Jonathan Berant
Retrieval-augmented language models (LMs) have received much attention recently. However, typically the retriever is not trained jointly as a native component of the LM, but added…
QAMPARI: An Open-domain Question Answering Benchmark for Questions with Many Answers from Multiple Paragraphs
Samuel Joseph Amouyal, Tomer Wolfson, Ohad Rubin +3
Existing benchmarks for open-domain question answering (ODQA) typically focus on questions whose answers can be extracted from a single paragraph. By contrast, many natural questio…
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…
SmBoP: Semi-autoregressive Bottom-up Semantic Parsing
Ohad Rubin, Jonathan Berant
The de-facto standard decoding method for semantic parsing in recent years has been to autoregressively decode the abstract syntax tree of the target program using a top-down depth…