Ensemble of Task-Specific Language Models for Brain Encoding

ML LM FT AI4S GSMEP PINNs
32.32w
2023年10月24日
语言模型已经被证明足够丰富，能够编码我们大脑特定感兴趣区域的fMRI激活。以往的研究已经探索了从为流行的自然语言处理任务学习到的表示中进行迁移学习，以预测大脑的反应。在我们的研究中，我们通过将10个流行的语言模型（2个句法和8个语义）创建成一个集成模型来提高这种编码器的性能。通过我们的集成方法，我们在所有感兴趣区域上平均超过当前基准线10%。
Language models have been shown to be rich enough to encode fMRI activations of certain Regions of Interest in our Brains. Previous works have explored transfer learning from representations learned for popular natural language processing tasks for predicting brain responses. In our work, we improve the performance of such encoders by creating an ensemble model out of 10 popular Language Models (2 syntactic and 8 semantic). We beat the current baselines by 10% on average across all ROIs through our ensembling methods.
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