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Keyword: adapters ×Clear all

3 claims from 2 papers

1. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Simple, Efficient, and Scalable Structure-Aware Adapter Boosts Protein Language Models

   Tan, Li, Zhou et al. · Journal of Chemical Information and Modeling · 2024

   The paper introduces SES-Adapter, a method that adds structural information to protein language models and is tested across several models, structure types and benchmark datasets.

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   1. UncheckedCompared with unmodified protein language models, SES-Adapter raised task performance by up to 11% (3% on average) and sped up training by up to 1034%.“Results show that compared to vanilla PLMs, SES-Adapter improves downstream task performance by a maximum of 11% and an average of 3%, with significantly accelerated training speed by a maximum of 1034% and an average of 362%, the convergence rate is also improved by approximately 2 times.”

2. Computer Science › Domain Adaptation and Few-Shot Learning

   Composable Sparse Fine-Tuning for Cross-Lingual Transfer

   Alan, Ponti, Korhonen and Vulić · arXiv (Cornell University) · 2021

   The paper introduces a sparse fine-tuning method using learned masks that can be composed for task and language, and reports it outperforms adapters in zero-shot cross-lingual transfer.

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   1. UncheckedThe paper's sparse fine-tuning method beats adapters by a large margin at zero-shot cross-lingual transfer on several multilingual benchmarks.“Most importantly, it outperforms adapters in zero-shot cross-lingual transfer by a large margin in a series of multilingual benchmarks, including Universal Dependencies, MasakhaNER, and AmericasNLI.”
   2. UncheckedThe authors report that keeping fine-tuning sparse is crucial to prevent both interference between combined fine-tunings and overfitting.“Based on an in-depth analysis, we additionally find that sparsity is crucial to prevent both 1) interference between the fine-tunings to be composed and 2) overfitting.”