Search Results for author: John Licato

Found 13 papers, 6 papers with code

No Strong Feelings One Way or Another: Re-operationalizing Neutrality in Natural Language Inference

no code implementations16 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.

Natural Language Inference

Resolving Open-textured Rules with Templated Interpretive Arguments

no code implementations19 Dec 2022 John Licato, Logan Fields, Zaid Marji

Open-textured terms in written rules are typically settled through interpretive argumentation.

Cognitive Modeling of Semantic Fluency Using Transformers

no code implementations20 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.


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?

How Should AI Interpret Rules? A Defense of Minimally Defeasible Interpretive Argumentation

no code implementations26 Oct 2021 John Licato

But here I refer to the kinds of rules expressed in human language that are the basis of laws, regulations, codes of conduct, ethical guidelines, and so on.

Improving Paraphrase Detection with the Adversarial Paraphrasing Task

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?

Paraphrase Identification Sentence

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

Probing the Natural Language Inference Task with Automated Reasoning Tools

1 code implementation6 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?

Natural Language Inference

Towards Concise, Machine-discovered Proofs of Gödel's Two Incompleteness Theorems

no code implementations6 May 2020 Elijah Malaby, Bradley Dragun, John Licato

There is an increasing interest in applying recent advances in AI to automated reasoning, as it may provide useful heuristics in reasoning over formalisms in first-order, second-order, or even meta-logics.

Automated Theorem Proving Vocal Bursts Valence Prediction

Scenarios and Recommendations for Ethical Interpretive AI

no code implementations5 Nov 2019 John Licato, Zaid Marji, Sophia Abraham

Artificially intelligent systems, given a set of non-trivial ethical rules to follow, will inevitably be faced with scenarios which call into question the scope of those rules.

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