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A Philosophical Introduction to Language Models - Part II: The Way Forward
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In this paper, the second of two companion pieces, we explore novel philosophical questions raised by recent progress in large language models (LLMs) that go beyond the classical debates covered in the first part. We focus particularly on issues related to interpretability, examining evidence from causal intervention methods about the nature of LLMs' internal representations and computations. We also discuss the implications of multimodal and modular extensions of LLMs, recent debates about whether such systems may meet minimal criteria for consciousness, and concerns about secrecy and reproducibility in LLM research. Finally, we discuss whether LLM-like systems may be relevant to modeling aspects of human cognition, if their architectural characteristics and learning scenario are adequately constrained.
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Explaining Neural Networks with Reasons
A new interpretability method computes 'reasons vectors' from neuron activations and measures how strongly each neuron supports propositions about the input, with experiments on MNIST, Adult, and SST2.
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