Rafael Li Chen


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MEGAnno+: A Human-LLM Collaborative Annotation System
Hannah Kim | Kushan Mitra | Rafael Li Chen | Sajjadur Rahman | Dan Zhang
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

Large language models (LLMs) can label data faster and cheaper than humans for various NLP tasks. Despite their prowess, LLMs may fall short in understanding of complex, sociocultural, or domain-specific context, potentially leading to incorrect annotations. Therefore, we advocate a collaborative approach where humans and LLMs work together to produce reliable and high-quality labels. We present MEGAnno+, a human-LLM collaborative annotation system that offers effective LLM agent and annotation management, convenient and robust LLM annotation, and exploratory verification of LLM labels by humans.


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MEGAnno: Exploratory Labeling for NLP in Computational Notebooks
Dan Zhang | Hannah Kim | Rafael Li Chen | Eser Kandogan | Estevam Hruschka
Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop (Language Advances)

We present MEGAnno, a novel exploratory annotation framework designed for NLP researchers and practitioners. Unlike existing labeling tools that focus on data labeling only, our framework aims to support a broader, iterative ML workflow including data exploration and model development. With MEGAnno’s API, users can programmatically explore the data through sophisticated search and automated suggestion functions and incrementally update task schema as their project evolve. Combined with our widget, the users can interactively sort, filter, and assign labels to multiple items simultaneously in the same notebook where the rest of the NLP project resides. We demonstrate MEGAnno’s flexible, exploratory, efficient, and seamless labeling experience through a sentiment analysis use case.