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Found 8 papers, 5 papers with code
Stronger Random Baselines for In-Context Learning
Evaluating the in-context learning classification performance of language models poses challenges due to small dataset sizes, extensive prompt-selection using the validation set, and intentionally difficult tasks that lead to near-random performance.
The Afterlives of Shakespeare and Company in Online Social Readership
Meanwhile, the digitization of the lending library records of Shakespeare and Company provides a window into the reading activity of an earlier, smaller community in interwar Paris.
Data Similarity is Not Enough to Explain Language Model Performance
Large language models achieve high performance on many but not all downstream tasks.
A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity
Second, we explore the effect of quality and toxicity filters, showing a trade-off between performance on standard benchmarks and risk of toxic generations.
Comparing Text Representations: A Theory-Driven Approach
Much of the progress in contemporary NLP has come from learning representations, such as masked language model (MLM) contextual embeddings, that turn challenging problems into simple classification tasks.
Domain-Specific Lexical Grounding in Noisy Visual-Textual Documents
Images can give us insights into the contextual meanings of words, but current image-text grounding approaches require detailed annotations.
Computational Histological Staining and Destaining of Prostate Core Biopsy RGB Images with Generative Adversarial Neural Networks
The staining model uses a conditional generative adversarial network that learns hierarchical non-linear mappings between whole slide RGB image (WSRI) pairs of prostate core biopsy before and after H&E staining.
Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health
We report an automated process that combines intraoral fluorescent porphyrin biomarker imaging, clinical examinations and machine learning for correlation of systemic health conditions with periodontal disease.
