1 code implementation • COLING 2022 • Oishik Chatterjee, Isha Pandey, Aashish Waikar, Vishwajeet Kumar, Ganesh Ramakrishnan
In order to address this challenge of equation annotation, we propose a weakly supervised model for solving MWPs by requiring only the final answer as supervision.
1 code implementation • 7 Feb 2025 • Saurabh Jha, Rohan Arora, Yuji Watanabe, Takumi Yanagawa, Yinfang Chen, Jackson Clark, Bhavya Bhavya, Mudit Verma, Harshit Kumar, Hirokuni Kitahara, Noah Zheutlin, Saki Takano, Divya Pathak, Felix George, Xinbo Wu, Bekir O. Turkkan, Gerard Vanloo, Michael Nidd, Ting Dai, Oishik Chatterjee, Pranjal Gupta, Suranjana Samanta, Pooja Aggarwal, Rong Lee, Pavankumar Murali, Jae-wook Ahn, Debanjana Kar, Ameet Rahane, Carlos Fonseca, Amit Paradkar, Yu Deng, Pratibha Moogi, Prateeti Mohapatra, Naoki Abe, Chandrasekhar Narayanaswami, Tianyin Xu, Lav R. Varshney, Ruchi Mahindru, Anca Sailer, Laura Shwartz, Daby Sow, Nicholas C. M. Fuller, Ruchir Puri
Our results show that agents powered by state-of-the-art models resolve only 13. 8% of SRE scenarios, 25. 2% of CISO scenarios, and 0% of FinOps scenarios.
no code implementations • 12 Sep 2024 • Oishik Chatterjee, Pooja Aggarwal, Suranjana Samanta, Ting Dai, Prateeti Mohapatra, Debanjana Kar, Ruchi Mahindru, Steve Barbieri, Eugen Postea, Brad Blancett, Arthur De Magalhaes
In the rapidly evolving landscape of site reliability engineering (SRE), the demand for efficient and effective solutions to manage and resolve issues in site and cloud applications is paramount.
no code implementations • 28 Aug 2024 • Pooja Aggarwal, Oishik Chatterjee, Ting Dai, Prateeti Mohapatra, Brent Paulovicks, Brad Blancett, Arthur De Magalhaes
The advent of large language models (LLMs) has greatly facilitated code generation, but ensuring the functional correctness of generated code remains a challenge.
no code implementations • 11 Aug 2022 • Oishik Chatterjee, Jaidam Ram Tej, Narendra Varma Dasaraju
We propose a novel approach which can use information from customer reviews along with customer and product features for size and fit predictions.
no code implementations • 14 Apr 2021 • Oishik Chatterjee, Isha Pandey, Aashish Waikar, Vishwajeet Kumar, Ganesh Ramakrishnan
We approach this problem by first learning to generate the equation using the problem description and the final answer, which we subsequently use to train a supervised MWP solver.
1 code implementation • Findings (ACL) 2021 • Ayush Maheshwari, Oishik Chatterjee, KrishnaTeja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer
The first contribution of this work is an introduction of a framework, \model which is a semi-supervised data programming paradigm that learns a \emph{joint model} that effectively uses the rules/labelling functions along with semi-supervised loss functions on the feature space.
2 code implementations • 22 Nov 2019 • Oishik Chatterjee, Ganesh Ramakrishnan, Sunita Sarawagi
Scarcity of labeled data is a bottleneck for supervised learning models.