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LIMIT: Less Is More for Instruction Tuning Across Evaluation Paradigms
We find that subsets of 1k-6k instruction finetuning samples are sufficient to achieve good performance on both (1) traditional NLP benchmarks and (2) model-based evaluation.
Bayesian Active Learning for Discrete Latent Variable Models
We show that our method substantially reduces the amount of data needed to fit GLM-HMM, and outperforms a variety of approximate methods based on variational and amortized inference.
Extracting low-dimensional psychological representations from convolutional neural networks
Deep neural networks are increasingly being used in cognitive modeling as a means of deriving representations for complex stimuli such as images.
Do Deep Neural Networks Model Nonlinear Compositionality in the Neural Representation of Human-Object Interactions?
Visual scene understanding often requires the processing of human-object interactions.
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