Data-Centric and Model-Centric Approaches for Biomedical Question Answering

Yoon, Wonjin; Yoo, Jaehyo; Seo, Sumin; Sung, Mujeen; Jeong, Minbyul; Kim, Gangwoo; Kang, Jaewoo

doi:10.1007/978-3-031-13643-6_16

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Developers and users of AI technology face various problems with far-reaching implications, and rectifying AI technology has become urgent amid its wide-scale adoption [54,55]. To this end, the amalgamation of DC-AI and MC-AI might be necessary to help AI technology contribute more to the social good [56]. It is worth noting that there exist interactive relationships between DC-AI and MC-AI because the changes in data result in changes in the code of AI models.…”

Section: Insight To When To Amalgamate Dc-ai With Mc-aimentioning

confidence: 99%

Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era

Majeed,

Hwang

2024

ASI

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Data-centric artificial intelligence (DC-AI) is a modern paradigm that gives more priority to data quality enhancement, rather than only optimizing the complex codes of AI models. The DC-AI paradigm is expected to substantially advance the status of AI research and developments, which has been solely based on model-centric AI (MC-AI) over the past 30 years. Until present, there exists very little knowledge about DC-AI, and its significance in terms of solving real-world problems remains unexplored in the recent literature. In this technical note, we present the core aspects of DC-AI and MC-AI and discuss their interplay when used to solve some real-world problems. We discuss the potential scenarios/situations that require the integration of DC-AI with MC-AI to solve challenging problems in AI. We performed a case study on a real-world dataset to corroborate the potential of DC-AI in realistic scenarios and to prove its significance over MC-AI when either data are limited or their quality is poor. Afterward, we comprehensively discuss the challenges that currently hinder the realization of DC-AI, and we list promising avenues for future research and development concerning DC-AI. Lastly, we discuss the next-generation computing for DC-AI that can foster DC-AI-related developments and can help transition DC-AI from theory to practice. Our detailed analysis can guide AI practitioners toward exploring the undisclosed potential of DC-AI in the current AI-driven era.

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Section: Insight To When To Amalgamate Dc-ai With Mc-aimentioning

confidence: 99%

Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era

Majeed,

Hwang

2024

ASI

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“…However, this might not hold if data are filtered in a random way (Firsanova, 2021). Additionally, while increasing labelling consistency and excluding or cleaning noisy data points were shown to improve model performance on the BioASQ dataset (Yoon et al, 2022), shortening answers in AASDQA led to a decrease of F1-score by 4% (Firsanova, 2021).…”

Section: Related Workmentioning

confidence: 99%

The Fourth Workshop on Insights from Negative Results in NLP

2023

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Amherst. His research focuses broadly on designing machine learning models for discourse-level language generation (e.g., for story generation and machine translation), and his group also works on tasks involving creative language understanding (e.g., modeling fictional narratives and characters). He is the recipient of best paper awards at NAACL (2016, 2018) and a best demo award at NeurIPS 2015, and he

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Section: Related Workmentioning

confidence: 99%

A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets

Bojic,

Halim,

Suharman

et al. 2023

The Fourth Workshop on Insights From Negative Results in NLP

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Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance. Highquality datasets are needed for general-purpose Large Language Models (LLMs) training, as well as for domain-specific models, which are usually small in size as it is costly to engage a large number of domain experts for their creation. Thus, it is vital to ensure high-quality domain-specific training data. In this paper, we propose a framework for enhancing the data quality of original datasets 1 . We applied the proposed framework to four biomedical datasets and showed relative improvement of up to 33%/40% for fine-tuning of retrieval/reader models on the BioASQ dataset when using back translation to enhance the original dataset quality.

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Data-Centric and Model-Centric Approaches for Biomedical Question Answering

Cited by 6 publications

References 20 publications

Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era

Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era

The Fourth Workshop on Insights from Negative Results in NLP

A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets

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