Chain-of-Thought reasoning in LLMs is often unfaithful, with models relying on it variably by task and less so as models scale larger.
URL https: //www.science.org/doi/abs/10.1126/sc irobotics.aay7120
4 Pith papers cite this work. Polarity classification is still indexing.
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Enhancing AI Interpretability and Safety through Localised Architectures
Authors argue by analogy that localised hardware ML architectures may be more interpretable and efficient than deep neural networks on GPUs for smaller datasets and evaluate candidate hardware paradigms.