2.5k citations · 2.7k across the 13 of their papers we have counts for
6 papers · 1 filter
Differentiable Tree Operations Promote Compositional Generalization
Paul Soulos, Edward Hu, Kate McCurdy +4
In the context of structure-to-structure transformation tasks, learning sequences of discrete symbolic operations poses significant challenges due to their non-differentiability. T…
Guided Generation of Cause and Effect
Zhongyang Li, Xiao Ding, Ting Liu +2
We present a conditional text generation framework that posits sentential expressions of possible causes and effects. This framework depends on two novel resources we develop in th…
LoRA: Low-Rank Adaptation of Large Language Models
Edward J. Hu, Yelong Shen, Phillip Wallis +5
An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larg…
Iterative Paraphrastic Augmentation with Discriminative Span Alignment
Ryan Culkin, J. Edward Hu, Elias Stengel-Eskin +2
We introduce a novel paraphrastic augmentation strategy based on sentence-level lexically constrained paraphrasing and discriminative span alignment. Our approach allows for the la…
ParaBank: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-constrained Neural Machine Translation
J. Edward Hu, Rachel Rudinger, Matt Post +1
We present ParaBank, a large-scale English paraphrase dataset that surpasses prior work in both quantity and quality. Following the approach of ParaNMT, we train a Czech-English ne…
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
Adam Poliak, Aparajita Haldar, Rachel Rudinger +4
We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types o…