2 citations · 4 across the 3 of their papers we have counts for
6 papers
Giving AI Personalities Leads to More Human-Like Reasoning
Animesh Nighojkar, Bekhzodbek Moydinboyev, My Duong +1
In computational cognitive modeling, capturing the full spectrum of human judgment and decision-making processes, beyond just optimal behaviors, is a significant challenge. This st…
No Strong Feelings One Way or Another: Re-operationalizing Neutrality in Natural Language Inference
Animesh Nighojkar, Antonio Laverghetta, John Licato
Natural Language Inference (NLI) has been a cornerstone task in evaluating language models' inferential reasoning capabilities. However, the standard three-way classification schem…
Predicting Human Psychometric Properties Using Computational Language Models
Antonio Laverghetta, Animesh Nighojkar, Jamshidbek Mirzakhalov +1
Transformer-based language models (LMs) continue to achieve state-of-the-art performance on natural language processing (NLP) benchmarks, including tasks designed to mimic human-in…
Improving Paraphrase Detection with the Adversarial Paraphrasing Task
Animesh Nighojkar, John Licato
If two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, m…
Can Transformer Language Models Predict Psychometric Properties?
Antonio Laverghetta, Animesh Nighojkar, Jamshidbek Mirzakhalov +1
Transformer-based language models (LMs) continue to advance state-of-the-art performance on NLP benchmark tasks, including tasks designed to mimic human-inspired "commonsense" comp…
Probing the Natural Language Inference Task with Automated Reasoning Tools
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, doe…