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Found 2 papers, 1 papers with code
Multi-Head Adapter Routing for Data-Efficient Fine-Tuning
In particular, we find that instruction tuning, where models are fully fine-tuned on natural language instructions for each task, is inferior to modular methods such as Polytropon and our proposed variants.
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition
To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way.
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