Fix your Models by Fixing your Datasets

Sanyal, Atindriyo; Chatterji, Vikram; Vyas, Nidhi; Epstein, Ben; Demir, N.; Corletti, Anthony

doi:10.48550/arxiv.2112.07844

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2023

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“…Several studies have reported that data filtering can lead to significant model improvements (Dou et al, 2020;Sanyal et al, 2021;Mollá, 2022). However, this might not hold if data are filtered in a random way (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%

The Fourth Workshop on Insights from Negative Results in NLP

2023

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Section: Related Workmentioning

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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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Fix your Models by Fixing your Datasets

Cited by 2 publications

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The Fourth Workshop on Insights from Negative Results in NLP

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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