1 citations · 2 across the 5 of their papers we have counts for
5 papers
Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter?
Nemika Tyagi, Mihir Parmar, Mohith Kulkarni +5
Solving grid puzzles involves a significant amount of logical reasoning. Hence, it is a good domain to evaluate the reasoning capability of a model which can then guide us to impro…
Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models
Nisarg Patel, Mohith Kulkarni, Mihir Parmar +4
As Large Language Models (LLMs) continue to exhibit remarkable performance in natural language understanding tasks, there is a crucial need to measure their ability for human-like…
LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models
Mihir Parmar, Nisarg Patel, Neeraj Varshney +5
Recently developed large language models (LLMs) have been shown to perform remarkably well on a wide range of language understanding tasks. But, can they really "reason" over the n…
Can NLP Models 'Identify', 'Distinguish', and 'Justify' Questions that Don't have a Definitive Answer?
Ayushi Agarwal, Nisarg Patel, Neeraj Varshney +7
Though state-of-the-art (SOTA) NLP systems have achieved remarkable performance on a variety of language understanding tasks, they primarily focus on questions that have a correct…
Can NLP Models Correctly Reason Over Contexts that Break the Common Assumptions?
Neeraj Varshney, Mihir Parmar, Nisarg Patel +4
Pre-training on large corpora of text enables the language models to acquire a vast amount of factual and commonsense knowledge which allows them to achieve remarkable performance…