LLM-Assisted Annotation

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17 papers in the last four weeks, up 143% on the four weeks before. 0.2% of all new papers.

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Transferring Natural Language Datasets Between Languages Using Large Language Models for Modern Decision Support and Sci-Tech Analytical Systems
Oct 17, 2024Dmitrii Popov, Egor Terentev, Danil Serenko +2LLM-Assisted Annotation Low-Resource Language Processing
Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences (FRC CSC RAS), Moscow 119333, Russia · Faculty of Physics and Mathematics and Natural Sciences, RUDN University, Moscow 117198, Russia · Institute for Information Transmission Problems of the Russian Academy of Sciences (IITP RAS), Moscow 127051, Russia +1
Which Demographics do LLMs Default to During Annotation?
Oct 11, 2024Johannes Schäfer, Aidan Combs, Christopher Bagdon +9LLM Prompting LLM-Assisted Annotation
Fundamentals of Natural Language Processing, University of Bamberg, Germany
"You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling
Mar 26, 2024Christopher Bagdon, Prathamesh Karmalker, Harsha Gurulingappa +1LLM-Assisted Annotation Emotion Recognition
Merck Data & AI Organization, Merck Group, Darmstadt, Germany · Institut für Maschinelle Sprachverarbeitung, University of Stuttgart, Germany · Fundamentals of Natural Language Processing, University of Bamberg, Germany +1
LLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domains
Date pendingRyogo Hishikawa, Ichiro Kataoka, Shinya YudaLLM-Assisted Annotation Recommender Systems
Hitachi, Ltd. Research and Development Group Hitachi, Ibaraki, Japan · Hitachi Power Solutions Co., Ltd. Hitachi, Ibaraki, Japan

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Transferring Natural Language Datasets Between Languages Using Large Language Models for Modern Decision Support and Sci-Tech Analytical Systems

Dmitrii Popov, Egor Terentev, Danil Serenko, Ilya Sochenkov+1

Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences (FRC CSC RAS), Moscow 119333, Russia · Faculty of Physics and Mathematics and Natural Sciences, RUDN University, Moscow 117198, Russia · Institute for Information Transmission Problems of the Russian Academy of Sciences (IITP RAS), Moscow 127051, Russia+1

LLM-Assisted Annotation Low-Resource Language Processing Cross-Lingual Transfer

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The decision-making process to rule R&D relies on information related to current trends in particular research areas. In this work, we investigated how one can use large language models (LLMs) to transfer the dataset and its annotation from one language to another. This is crucial since sharing knowledge between different languages could boost certain underresourced directions in the target language, saving lots of effort in data annotation or quick prototyping. We experiment with English and Russian pairs, translating the DEFT (Definition Extraction from Texts) corpus. This corpus contains three layers of annotation dedicated to term-definition pair mining, which is a rare annotation type for Russian. The presence of such a dataset is beneficial for the natural language processing methods of trend analysis in science since the terms and definitions are the basic blocks of any scientific field. We provide a pipeline for the annotation transfer using LLMs. In the end, we train the BERT-based models on the translated dataset to establish a baseline.

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Latest papers 124

Transferring Natural Language Datasets Between Languages Using Large Language Models for Modern Decision Support and Sci-Tech Analytical Systems

Which Demographics do LLMs Default to During Annotation?

"You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling

LLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domains