Publication Date
2024
Publication Title
PROCEEDINGS OF THE EIGHTH WORKSHOP ON DATA MANAGEMENT FOR END-TO-END MACHINE LEARNING
Abstract
Experts outside the field of machine learning (ML) are interested in using ML techniques to analyze their textual data, but they are inhibited by a lack of convenient natural language processing (NLP) tools. To address this issue, we present tailwiz, an easy-to-use Python tool, powered by supervised fine-tuning of NLP models. tailwiz caters to domain experts by abstracting away technical ML knowledge and running conveniently on personal computers, the preferred mode of computation among domain experts. We show that tailwiz outperforms domain experts’ current textual analysis techniques on a majority of real-world tasks, up to a 384.8% F1 increase (46.18% absolute increase). tailwiz consistently outperforms GPT-3.5-Turbo on such tasks, showing the need for fine-tuned NLP models to perform domain-specific tasks that meet the analytical demands of domain experts.
Recommended Citation
Austin Peters, Tim Daj, Jonah Gelbach, David Freeman Engstrom & Daniel Khang, "tailwiz: Empowering Domain Experts with Easy-to-Use, Task-Specific Natural Language Processing Models," 2024 PROCEEDINGS OF THE EIGHTH WORKSHOP ON DATA MANAGEMENT FOR END-TO-END MACHINE LEARNING 12 (2024).
