71 citations · 143 across the 14 of their papers we have counts for
14 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…
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…
Orca: Progressive Learning from Complex Explanation Traces of GPT-4
Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar +3
Recent research has focused on enhancing the capability of smaller models through imitation learning, drawing on the outputs generated by large foundation models (LFMs). A number o…
Instruction Tuned Models are Quick Learners
Himanshu Gupta, Saurabh Arjun Sawant, Swaroop Mishra +4
Instruction tuning of language models has demonstrated the ability to enhance model generalization to unseen tasks via in-context learning using a few examples. However, typical su…
NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks
Swaroop Mishra, Arindam Mitra, Neeraj Varshney +4
Given the ubiquitous nature of numbers in text, reasoning with numbers to perform simple calculations is an important skill of AI systems. While many datasets and models have been…
Improving Biomedical Information Retrieval with Neural Retrievers
Man Luo, Arindam Mitra, Tejas Gokhale +1
Information retrieval (IR) is essential in search engines and dialogue systems as well as natural language processing tasks such as open-domain question answering. IR serve an impo…