60 citations · 302 across the 41 of their papers we have counts for
5 papers · 1 filter
Making Pre-trained Language Models Better Few-shot Learners
Tianyu Gao, Adam Fisch, Danqi Chen
The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context.…
Learning Dense Representations of Phrases at Scale
Jinhyuk Lee, Mujeen Sung, Jaewoo Kang +1
Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019). However,…
TextHide: Tackling Data Privacy in Language Understanding Tasks
Yangsibo Huang, Zhao Song, Danqi Chen +2
An unsolved challenge in distributed or federated learning is to effectively mitigate privacy risks without slowing down training or reducing accuracy. In this paper, we propose Te…
A Frustratingly Easy Approach for Entity and Relation Extraction
Zexuan Zhong, Danqi Chen
End-to-end relation extraction aims to identify named entities and extract relations between them. Most recent work models these two subtasks jointly, either by casting them in one…
Dense Passage Retrieval for Open-Domain Question Answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min +5
Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de…