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Found 3 papers, 1 papers with code
Learned feature representations are biased by complexity, learning order, position, and more
These results also highlight a key challenge for interpretability $-$ or for comparing the representations of models and brains $-$ disentangling extraneous biases from the computationally important aspects of a system's internal representations.
Self-Refine: Iterative Refinement with Self-Feedback
Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement.
Data for free: Fewer-shot algorithm learning with parametricity data augmentation
We address the problem of teaching an RNN to approximate list-processing algorithms given a small number of input-output training examples.
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