194 citations · 561 across the 9 of their papers we have counts for
6 papers · 1 filter
True Few-Shot Learning with Language Models
Ethan Perez, Douwe Kiela, Kyunghyun Cho
Pretrained language models (LMs) perform well on many tasks even when learning from a few examples, but prior work uses many held-out examples to tune various aspects of learning,…
Case-based Reasoning for Natural Language Queries over Knowledge Bases
Rajarshi Das, Manzil Zaheer, Dung Thai +6
It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions -- a paradigm known as case-based reas…
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus +9
Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. Howe…
Unsupervised Question Decomposition for Question Answering
Ethan Perez, Patrick Lewis, Wen-tau Yih +2
We aim to improve question answering (QA) by decomposing hard questions into simpler sub-questions that existing QA systems are capable of answering. Since labeling questions with…
Finding Generalizable Evidence by Learning to Convince Q&A Models
Ethan Perez, Siddharth Karamcheti, Rob Fergus +3
We propose a system that finds the strongest supporting evidence for a given answer to a question, using passage-based question-answering (QA) as a testbed. We train evidence agent…
ELI5: Long Form Question Answering
Angela Fan, Yacine Jernite, Ethan Perez +3
We introduce the first large-scale corpus for long-form question answering, a task requiring elaborate and in-depth answers to open-ended questions. The dataset comprises 270K thre…