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（2024）

Megagon Labs

Cited 1|Views20

Abstract

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

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Key words

Language Modeling,Description Logics,Neural Machine Translation,Schema Matching,Multilingual Neural Machine Translation

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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