DrBenchmark: The First-Ever Publicly Available French Biomedical Large Language Understanding Benchmark
https://lnkd.in/e2Mb27Ez
Introducing DrBenchmark: The First-Ever French Biomedical Language Understanding Benchmark
A team of researchers in France has developed DrBenchmark to evaluate masked language models in French, specifically in the biomedical domain. With the rapid advancement of NLP, evaluating these models has become challenging due to variations in evaluation protocols.
Challenges in NLP Evaluation
Evaluating language models in the biomedical domain in languages other than English and Chinese has presented significant challenges. This has created a gap in accurately assessing the latest French biomedical models.
Practical Solutions and Value
DrBenchmark is the first publicly available French biomedical language understanding benchmark, consisting of 20 diverse tasks such as named-entity recognition, part-of-speech tagging, question-answering, semantic textual similarity, and classification. The key contribution of DrBenchmark lies in its combination of various tasks into a single benchmark, enabling the comprehensive assessment of pre-trained language models from different perspectives.
Automated Protocol for Fair Comparison
DrBenchmark provides a modular, reproducible, and easily customizable automated protocol for fair comparison among language models. It uses HuggingFace Datasets and the Transformers library for data loading, pre-training, and evaluation. The experimental protocol ensures consistency by fine-tuning all models using the same hyperparameters for each task.
Insights and Implications
Experiment results demonstrate that no single model excels across all tasks, emphasizing the need for domain-specific models to achieve optimal performance in the biomedical field. While French biomedical models perform exceptionally well in most tasks, certain out-of-domain or differently language-trained models maintain competitiveness in specific tasks.
AI Solutions for Business
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CTO @ Nym
1moInsightful! At Nym I believe we are one of the few places that have linguists and ML researchers (and also ML researchers with linguistics background) and I'm always amazed with the creative solutions we create around it and how much it impacts the way we see data. A lot of the points at the paper really resonate with me.