Search Results for author: Jamshidbek Mirzakhalov

Found 8 papers, 4 papers with code

Can Transformer Language Models Predict Psychometric Properties?

1 code implementation Joint Conference on Lexical and Computational Semantics 2021 Antonio Laverghetta Jr., Animesh Nighojkar, Jamshidbek Mirzakhalov, John Licato

We then use the responses to calculate standard psychometric properties of the items in the diagnostic test, using the human responses and the LM responses separately.

Towards a Task-Agnostic Model of Difficulty Estimation for Supervised Learning Tasks

1 code implementation Asian Chapter of the Association for Computational Linguistics 2020 Antonio Laverghetta Jr., Jamshidbek Mirzakhalov, John Licato

Curriculum learning, a training strategy where training data are ordered based on their difficulty, has been shown to improve performance and reduce training time on various NLP tasks.

Natural Language Inference

Predicting Human Psychometric Properties Using Computational Language Models

no code implementations12 May 2022 Antonio Laverghetta Jr., Animesh Nighojkar, Jamshidbek Mirzakhalov, John Licato

In other words, can LMs be of use in predicting the psychometric properties of test items, when those items are given to human participants?

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