Arindam Mitra

Crafting next-generation foundation models that are not only powerful and trustworthy but also customizable and efficient, catering to a diverse range of existing and emerging applications. My work has been integrated into Bing and Science Engine, the latter of which empowers scientists across multiple enterprises with cutting-edge AI products.

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Research

I am Arindam, a tiny timid soul born amidst another tiny timid one named Oodlabari, a village that I call home. Deep fog amuses me and probably that's why I find myself immersed in the boundless realm of artificial intelligence. My zeal for natural language understanding has driven me to explore how machines can acquire knowledge and effectively reason with it, forming the core of my research. Previously, I developed hybrid systems that integrated machine learning with formal representation and reasoning. However, the impressive capabilities of LLMs and neural models to store knowledge within their weights and reason using additional contextual information have led me to question the necessity of separate tools for formal reasoning. My recent research efforts involve building large foundation models that excel at learning from instructions, can follow detailed guidelines, and reason with utmost precision. I consider myself fortunate to be working on the problems I am passionate about at Microsoft Research, where I am part of an exceptional team that shares my enthusiasm for natural language understanding and large-scale models.

Learning From Instructions/Prompting
Orca: Progressive Learning from Complex Explanation Traces of GPT-4
Subhabrata Mukherjee*, Arindam Mitra*, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, Ahmed Awadallah
Arxiv
Instruction Tuned Models are Quick Learners
Himanshu Gupta, Saurabh Arjun Sawant, Swaroop Mishra, Mutsumi Nakamura, Arindam Mitra, Santosh Mashetty, Chitta Baral
Arxiv
Neural Reasoning
Improving biomedical information retrieval with neural retrievers
Man Luo, Arindam Mitra, Tejas Gokhale, Chitta Baral
AAAI Conference on Artificial Intelligence, 2022
Commonsense Reasoning with Implicit Knowledge in Natural Language
Pratyay Banerjee, Swaroop Mishra*, Kuntal Kumar Pal*, Arindam Mitra*, Chitta Baral
Automated Knowledge Base Construction (AKBC), 2021
Deeply embedded knowledge representation & reasoning for natural language question answering: A practitioner's perspective
Arindam Mitra, Sanjay Narayana, Chitta Baral
Workshop on Structured Prediction for NLP, 2020
Enhancing natural language inference using new and expanded training data sets and new learning models
Arindam Mitra, Ishan Shrivastava, Chitta Baral
AAAI Conference on Artificial Intelligence, 2020
Careful selection of knowledge to solve open book question answering
Pratyay Banerjee, Kuntal Kumar Pal*, Arindam Mitra*, Chitta Baral
Annual Meeting of the Association for Computational Linguistics (ACL), 2019
Hybrid Reasoning
Declarative Question Answering over Knowledge Bases Containing Natural Language Text with Answer Set Programming
Arindam Mitra, Peter Clark, Oyvind Tafjord, Chitta Baral
Proceedings of the AAAI Conference on Artificial Intelligence, 2019
Combining knowledge hunting and neural language models to solve the Winograd schema challenge
Ashok Prakash, Arpit Sharma, Arindam Mitra, Chitta Baral
Annual Meeting of the Association for Computational Linguistics (ACL), 2019
A generate-validate approach to answering questions about qualitative relationships
Arindam Mitra, Chitta Baral, Aurgho Bhattacharjee, , Ishan Shrivastava
Arxiv, 2019
Learning To Use Formulas To Solve Simple Arithmetic Problems
Arindam Mitra, Chitta Baral
Annual Meeting of the Association for Computational Linguistics (ACL), 2016
Learning to automatically solve logic grid puzzles
Arindam Mitra, Chitta Baral
Empirical Methods in Natural Language Processing (EMNLP), 2015
The NL2KR platform for building natural language translation systems
Nguyen Vo Arindam Mitra, Chitta Baral,
Annual Meeting of the Association for Computational Linguistics (ACL), 2015
Inductive Learning
Incremental and iterative learning of answer set programs from mutually distinct examples
Arindam Mitra, Chitta Baral
Theory and Practice of Logic Programming, 2018 (Jounral Publication)
Addressing a question answering challenge by combining statistical methods with inductive rule learning and reasoning
Arindam Mitra, Chitta Baral
AAAI Conference on Artificial Intelligence, 2016
Datasets
NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks
Swaroop Mishra, Arindam Mitra, Neeraj Varshney, Bhavdeep Sachdeva, Peter Clark, Chitta Baral, Ashwin Kalyan
Annual Meeting of the Association for Computational Linguistics (ACL), 2022
Enhancing natural language inference using new and expanded training data sets and new learning models
Arindam Mitra, Ishan Shrivastava, Chitta Baral
AAAI Conference on Artificial Intelligence, 2020
Declarative Question Answering over Knowledge Bases Containing Natural Language Text with Answer Set Programming
Arindam Mitra, Peter Clark, Oyvind Tafjord, Chitta Baral
AAAI Conference on Artificial Intelligence, 2019

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