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20162022
most citedFew-shot Sequence Learning with Transformers

5 citations · 5 across the 2 of their papers we have counts for

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cs.CL20242 cited

Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense

Siqi Shen, Lajanugen Logeswaran, Moontae Lee +3

Large language models (LLMs) have demonstrated substantial commonsense understanding through numerous benchmark evaluations. However, their understanding of cultural commonsense re…

cs.CL2022

Few-shot Subgoal Planning with Language Models

Lajanugen Logeswaran, Yao Fu, Moontae Lee +1

Pre-trained large language models have shown successful progress in many language understanding benchmarks. This work explores the capability of these models to predict actionable…

cs.CL2019

Zero-Shot Entity Linking by Reading Entity Descriptions

Lajanugen Logeswaran, Ming-Wei Chang, Kenton Lee +3

We present the zero-shot entity linking task, where mentions must be linked to unseen entities without in-domain labeled data. The goal is to enable robust transfer to highly speci…

cs.CL2018

Content preserving text generation with attribute controls

Lajanugen Logeswaran, Honglak Lee, Samy Bengio

In this work, we address the problem of modifying textual attributes of sentences. Given an input sentence and a set of attribute labels, we attempt to generate sentences that are…

cs.CL2018

An efficient framework for learning sentence representations

Lajanugen Logeswaran, Honglak Lee

In this work we propose a simple and efficient framework for learning sentence representations from unlabelled data. Drawing inspiration from the distributional hypothesis and rece…