no code implementations • 16 Jun 2023 • Animesh Nighojkar, Antonio Laverghetta Jr., John Licato
Natural Language Inference (NLI) has been a cornerstone task in evaluating language models' inferential reasoning capabilities.
no code implementations • 20 Aug 2022 • Animesh Nighojkar, Anna Khlyzova, John Licato
We report preliminary evidence suggesting that, despite obvious implementational differences in how people and TLMs learn and use language, TLMs can be used to identify individual differences in human fluency task behaviors better than existing computational models, and may offer insights into human memory retrieval strategies -- cognitive process not typically considered to be the kinds of things TLMs can model.
no code implementations • 12 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?
1 code implementation • ACL 2021 • Animesh Nighojkar, John Licato
Can we teach them instead to identify paraphrases in a way that draws on the inferential properties of the sentences, and is not over-reliant on lexical and syntactic similarities of a sentence pair?
Ranked #1 on Paraphrase Identification on AP
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.
1 code implementation • 6 May 2020 • Zaid Marji, Animesh Nighojkar, John Licato
The Natural Language Inference (NLI) task is an important task in modern NLP, as it asks a broad question to which many other tasks may be reducible: Given a pair of sentences, does the first entail the second?