6 citations · 9 across the 9 of their papers we have counts for
16 papers · 1 filter
\llinstruct: An Instruction-tuned model for English Language Proficiency Assessments
Debanjan Ghosh, Sophia Chan
We present \llinstruct: An 8B instruction-tuned model that is designed to generate content for English Language Proficiency Assessments (ELPA) and related applications. Our work in…
Identifying Fairness Issues in Automatically Generated Testing Content
Kevin Stowe, Benny Longwill, Alyssa Francis +3
Natural language generation tools are powerful and effective for generating content. However, language models are known to display bias and fairness issues, making them impractical…
The Benefits of Label-Description Training for Zero-Shot Text Classification
Lingyu Gao, Debanjan Ghosh, Kevin Gimpel
Pretrained language models have improved zero-shot text classification by allowing the transfer of semantic knowledge from the training data in order to classify among specific lab…
Controlled Language Generation for Language Learning Items
Kevin Stowe, Debanjan Ghosh, Mengxuan Zhao
This work aims to employ natural language generation (NLG) to rapidly generate items for English language learning applications: this requires both language models capable of gener…
AGReE: A system for generating Automated Grammar Reading Exercises
Sophia Chan, Swapna Somasundaran, Debanjan Ghosh +1
We describe the AGReE system, which takes user-submitted passages as input and automatically generates grammar practice exercises that can be completed while reading. Multiple-choi…
"What makes a question inquisitive?" A Study on Type-Controlled Inquisitive Question Generation
Lingyu Gao, Debanjan Ghosh, Kevin Gimpel
We propose a type-controlled framework for inquisitive question generation. We annotate an inquisitive question dataset with question types, train question type classifiers, and fi…