Papers by Abheesht Sharma
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts (2022.acl-demo)
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Stephen Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-david, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Fries, Maged Al-shaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Dragomir Radev, Mike Tian-jian Jiang, Alexander Rush
| Challenge: | PromptSource is a system for creating, sharing, and using natural language prompts . prompts are used to train and query language models in zero-shot learning settings . |
| Approach: | PromptSource is a system for creating, sharing, and using natural language prompts . et al.: using prompts to train and query language models is emerging area in NLP . they propose a templating language for defining data-linked prompts, a user interface that iterates on prompt development . |
| Outcome: | PromptSource is a system for creating, sharing, and using natural language prompts . it has a templating language for defining data-linked prompts and a community-driven set of guidelines . |
DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature (2021.emnlp-demo)
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| Challenge: | a tool for the diachronic analysis of research corpora is open source . the tool is based on well-cited research, with some of our own methods added for good measure. |
| Approach: | They propose an application for the diachronic analysis of research corpora . they collate key words from well-cited research and add some of their own methods . |
| Outcome: | The proposed tool is open source and easy to use. |
AnEMIC: A Framework for Benchmarking ICD Coding Models (2022.emnlp-demos)
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| Challenge: | Diagnostic coding is the task of assigning diagnosis codes defined by the ICD (International Classification of Diseases) standard to patient visits based on clinical notes. |
| Approach: | They propose to use an ICD coding framework to train and benchmark models . they correct errors in preprocessing and provide an interactive demo to analyze the models based on custom inputs. |
| Outcome: | The framework corrects errors in preprocessing and provides key models and weights trained on correctly preprocessed datasets. |