activity
20172022
most citedScaling Language Models: Methods, Analysis & Insights from Training Gopher

243 citations · 290 across the 7 of their papers we have counts for

collaborators

10 papers

cs.CL20225 cited

Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder

Fangyu Lei, Shizhu He, Xiang Li +2

In the real-world question answering scenarios, hybrid form combining both tabular and textual contents has attracted more and more attention, among which numerical reasoning probl…

cs.CL2022

Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding

Jianing Wang, Wenkang Huang, Qiuhui Shi +4

Knowledge-enhanced Pre-trained Language Model (PLM) has recently received significant attention, which aims to incorporate factual knowledge into PLMs. However, most existing metho…

cs.CL2022243 cited

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Jack W. Rae, Sebastian Borgeaud, Trevor Cai +77

Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.…

cs.AI20212 cited

Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning

Xuelu Chen, Michael Boratko, Muhao Chen +3

Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for ea…

cs.LG202022 cited

Improving Local Identifiability in Probabilistic Box Embeddings

Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang +3

Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment. Box embeddings, where objects are repr…

cs.CL20204 cited

Reading Comprehension as Natural Language Inference: A Semantic Analysis

Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar +3

In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited…