Taewoon Kim


2022

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PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
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
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

PromptSource is a system for creating, sharing, and using natural language prompts. Prompts are functions that map an example from a dataset to a natural language input and target output. Using prompts to train and query language models is an emerging area in NLP that requires new tools that let users develop and refine these prompts collaboratively. PromptSource addresses the emergent challenges in this new setting with (1) a templating language for defining data-linked prompts, (2) an interface that lets users quickly iterate on prompt development by observing outputs of their prompts on many examples, and (3) a community-driven set of guidelines for contributing new prompts to a common pool. Over 2,000 prompts for roughly 170 datasets are already available in PromptSource. PromptSource is available at https://github.com/bigscience-workshop/promptsource.

2021

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EMISSOR: A platform for capturing multimodal interactions as Episodic Memories and Interpretations with Situated Scenario-based Ontological References
Selene Baez Santamaria | Thomas Baier | Taewoon Kim | Lea Krause | Jaap Kruijt | Piek Vossen
Proceedings of the 1st Workshop on Multimodal Semantic Representations (MMSR)

We present EMISSOR: a platform to capture multimodal interactions as recordings of episodic experiences with explicit referential interpretations that also yield an episodic Knowledge Graph (eKG). The platform stores streams of multiple modalities as parallel signals. Each signal is segmented and annotated independently with interpretation. Annotations are eventually mapped to explicit identities and relations in the eKG. As we ground signal segments from different modalities to the same instance representations, we also ground different modalities across each other. Unique to our eKG is that it accepts different interpretations across modalities, sources and experiences and supports reasoning over conflicting information and uncertainties that may result from multimodal experiences. EMISSOR can record and annotate experiments in virtual and real-world, combine data, evaluate system behavior and their performance for preset goals but also model the accumulation of knowledge and interpretations in the Knowledge Graph as a result of these episodic experiences.